Relevant bibliographies by topics / Movie recommendation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Movie recommendation'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Movie recommendation"

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Shishodia, Dinesh. "Movie Recommendation System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4919–24. http://dx.doi.org/10.22214/ijraset.2021.35929.

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This paper represents the overview of Approaches and techniques used in Movie Recommendation system. Recommendation system is used by many companies like Netflix, Amazon, Flipkart etc. It makes the user experience better and decrease the user efforts. It plays a very vital role in our day-to-day life. It is used in recommending Movies, Articles, News, Books, Music, Videos, People (Online Dating) etc. It learns from the user past behavior and based on that behavior it recommends item to the user. Likewise, in Movie Recommendation system movie is recommended to the user on the basis of movies watched, liked, rated by the user. In year 2020, approximate 10,000 movie were launched according to IDMB data. It saves a lot of times and efforts of the user by suggesting movies according to user taste and user don’t have to select a movie from a large set of movies.
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Manavi, Vallari, Anjali Diwate, Priyanka Korade, and Anita Senathi. "MoView Engine : An Open Source Movie Recommender." ITM Web of Conferences 32 (2020): 03008. http://dx.doi.org/10.1051/itmconf/20203203008.

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Recommendation is an ideology that works as choice-based system for the end users. Users are recommended with their favorite movies based on history of other watched movies or based on the category of the movies. These types of recommendations are becoming popular because of their ability to think and react as human brain. For this purpose, deep learning or artificial intelligence comes into picture. It is the ability to think as a human brain as give the output best suited to the end users liking. This paper focuses on implementing the recommendation system of movies using deep learning with neural network model using the activation function of SoftMax to give an experience to users as friendly recommendation. Moreover, this paper focuses on different scenarios of recommendation like the recommendation based on history, genre of the movie etc.
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Li, Bo, Yibin Liao, and Zheng Qin. "Precomputed Clustering for Movie Recommendation System in Real Time." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/742341.

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A recommendation system delivers customized data (articles, news, images, music, movies, etc.) to its users. As the interest of recommendation systems grows, we started working on the movie recommendation systems. Most research efforts in the fields of movie recommendation system are focusing on discovering the most relevant features from users, or seeking out users who share same tastes as that of the given user as well as recommending the movies according to the liking of these sought users or seeking out users who share a connection with other people (friends, classmates, colleagues, etc.) and make recommendations based on those related people’s tastes. However, little research has focused on recommending movies based on the movie’s features. In this paper, we present a novel idea that applies machine learning techniques to construct a cluster for the movie by implementing a distance matrix based on the movie features and then make movie recommendation in real time. We implement some different clustering methods and evaluate their performance in a real movie forum website owned by one of our authors. This idea can also be used in other types of recommendation systems such as music, news, and articles.
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B, Adithya. "Movie Recommendation System." International Journal for Research in Applied Science and Engineering Technology 8, no. 11 (November 30, 2020): 120–22. http://dx.doi.org/10.22214/ijraset.2020.32064.

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., Darshini M., Abishay Raina ., Rakshit Mysore Lokesh ., Mohammed Noorulla Khan Durrani ., and T. H. Sreenivas . "MOVIE RECOMMENDATION SYSTEM." International Journal of Engineering Applied Sciences and Technology 03, no. 11 (March 31, 2019): 39–41. http://dx.doi.org/10.33564/ijeast.2019.v03i11.008.

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Raj, Kunal, Atulya Abhinav Das, Antariksh Guha, Parth Sharma, and Mohana Kumar S. "Movie Recommendation System." International Journal of Computer Sciences and Engineering 7, no. 4 (April 30, 2019): 1024–28. http://dx.doi.org/10.26438/ijcse/v7i4.10241028.

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Verma, Rupal. "Movie Recommendation System by Using Collaborative Filtering." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 888–92. http://dx.doi.org/10.22214/ijraset.2021.38084.

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Abstract: This is the era of modern technology where we are all surrounded and covered by technology. This eases our daily life and saves our time and one of the most important techniques that played a very important role in our day-to-day life is the recommendation system. The recommendation system is used in various fields like it is used to recommend products, books, videos, movies, news, and many more. In this paper, we use a Recommendation system for movies we built or a movie recommendation system. It is based on a collaborative filtering approach that makes use of the information provided by the users, analyzes them and recommends movies according to the taste of users. The recommended movie list sorted according to the ratings given to this system is developed in python by using pycharm IDE and MYSQL for database connectivity. The presented recommendation system generates recommendations using various types of knowledge and data about users. Our Recommendation system recommends movies to each and every user by their previous searching history. Here we use some searching techniques as well. We also tried to overcome the cold start problem we use Movielens database. Keywords: Collaborative-filtering, Content-based filtering, Clustering, Recommendation system searching technique, Movies
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Komurlekar, Runali. "Movie Recommendation Model from Data through Online Streaming." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1549–51. http://dx.doi.org/10.22214/ijraset.2021.37495.

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Abstract: With the Pandemic era and easy availability of internet, potential of digital movie and tv series industry is in huge demand. Hence it has led to developing an automatic movie recommendation engine and has become a popular issue. Some of these problems can be solved or at least be minimized if we take the right decisions on what kind of movies to ignore, what movies to consider. This paper examines the recommendations that are obtained with considering the sample movies that have never got an above-average rating, where average rating is defined here as the mid-value between 0 and maximum rating used, for example, 2.5 in 1 to 5 rating scale. The technique used is “collaborative filtering”. Comparison of different pre-training model, it is tried to maximize the effectiveness of semantic understanding and make the recommendation be able to reflect meticulous perception on the relationship between user utilisation and user preference. Keywords: movie recommendation system, user similarity, user similarity, consumption pattern
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Liu, Duen-Ren, Yun-Cheng Chou, and Ciao-Ting Jian. "Integrating collaborative topic modeling and diversity for movie recommendations during news browsing." Kybernetes 49, no. 11 (November 27, 2019): 2633–49. http://dx.doi.org/10.1108/k-08-2019-0578.

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Purpose Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles. Design/methodology/approach Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website. Findings The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance. Originality/value Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.
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Ibrahim, Muhammad, and Imran Bajwa. "Design and Application of a Multi-Variant Expert System Using Apache Hadoop Framework." Sustainability 10, no. 11 (November 19, 2018): 4280. http://dx.doi.org/10.3390/su10114280.

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Movie recommender expert systems are valuable tools to provide recommendation services to users. However, the existing movie recommenders are technically lacking in two areas: first, the available movie recommender systems give general recommendations; secondly, existing recommender systems use either quantitative (likes, ratings, etc.) or qualitative data (polarity score, sentiment score, etc.) for achieving the movie recommendations. A novel approach is presented in this paper that not only provides topic-based (fiction, comedy, horror, etc.) movie recommendation but also uses both quantitative and qualitative data to achieve a true and relevant recommendation of a movie relevant to a topic. The used approach relies on SentiwordNet and tf-idf similarity measures to calculate the polarity score from user reviews, which represent the qualitative aspect of likeness of a movie. Similarly, three quantitative variables (such as likes, ratings, and votes) are used to get final a recommendation score. A fuzzy logic module decides the recommendation category based on this final recommendation score. The proposed approach uses a big data technology, “Hadoop” to handle data diversity and heterogeneity in an efficient manner. An Android application collaborates with a web-bot to use recommendation services and show topic-based recommendation to users.
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Dissertations / Theses on the topic "Movie recommendation"

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Bhargav, Suvir. "Efficient Features for Movie Recommendation Systems." Thesis, KTH, Kommunikationsteori, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-155137.

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User written movie reviews carry substantial amounts of movie related features such as description of location, time period, genres, characters, etc. Using natural language processing and topic modeling based techniques, it is possible to extract features from movie reviews and find movies with similar features. In this thesis, a feature extraction method is presented and the use of the extracted features in finding similar movies is investigated. We do the text pre-processing on a collection of movie reviews. We then extract topics from the collection using topic modeling techniques and store the topic distribution for each movie. Similarity metrics such as Hellinger distance is then used to find movies with similar topic distribution. Furthermore, the extracted topics are used as an explanation during subjective evaluation. Experimental results show that our extracted topics represent useful movie features and that they can be used to find similar movies efficiently.
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Kirmemis, Oznur. "Openmore: A Content-based Movie Recommendation System." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609479/index.pdf.

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The tremendous growth of Web has made information overload problem increasingly serious. Users are often confused by huge amount of information available on the internet and they are faced with the problem of finding the most relevant information that meets their needs. Recommender systems have proven to be an important solution approach to this problem. This thesis will present OPENMORE, a movie recommendation system, which is primarily based on content-based filtering technique. The distinctive point of this study lies in the methodology used to construct and update user and item profiles and the optimizations used to fine-tune the constructed user models. The proposed system arranges movie content data as features of a set of dimension slots, where each feature is assigned a stable feature weight regardless of individual movies. These feature weights and the explicit feedbacks provided by the user are then used to construct the user profile, which is fine-tuned through a set of optimization mechanisms. Users are enabled to view their profile, update them and create multiple contexts where they can provide negative and positive feedback for the movies on the feature level.
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Cakiroglu, Seda. "Suggest Me A Movie: A Multi-client Movie Recommendation Application On Facebook." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612084/index.pdf.

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In this study, an online movie recommendation engine that serves on Facebook is developed in order to evaluate social circle eects on user preferences in a trust-based environment. Instead of using single-user profiles in the social environment identification process, virtual group profiles that present common tastes of the social environments, are formed to achieve a successful social circle analysis and innovative suggestions. Recommendations are generated based on similar social circles and based on social circles of similar users separately and their results are evaluated. Pure collaborative filtering is applied to emphasize the influence of social environment characteristics.
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Larsson, Carl-Johan. "Movie Recommendation System Using Large Scale Graph-Processing." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200601.

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Karaman, Hilal. "A Content Based Movie Recommendation System Empowered By Collaborative Missing Data Prediction." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612037/index.pdf.

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The evolution of the Internet has brought us into a world that represents a huge amount of information items such as music, movies, books, web pages, etc. with varying quality. As a result of this huge universe of items, people get confused and the question &ldquo
Which one should I choose?&rdquo
arises in their minds. Recommendation Systems address the problem of getting confused about items to choose, and filter a specific type of information with a specific information filtering technique that attempts to present information items that are likely of interest to the user. A variety of information filtering techniques have been proposed for performing recommendations, including content-based and collaborative techniques which are the most commonly used approaches in recommendation systems. This thesis work introduces ReMovender, a content-based movie recommendation system which is empowered by collaborative missing data prediction. The distinctive point of this study lies in the methodology used to correlate the users in the system with one another and the usage of the content information of movies. ReMovender makes it possible for the users to rate movies in a scale from one to five. By using these ratings, it finds similarities among the users in a collaborative manner to predict the missing ratings data. As for the content-based part, a set of movie features are used in order to correlate the movies and produce recommendations for the users.
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Ozbal, Gozde. "A Content Boosted Collaborative Filtering Approach For Movie Recommendation Based On Local &amp." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610984/index.pdf.

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Recently, it has become more and more difficult for the existing web based systems to locate or retrieve any kind of relevant information, due to the rapid growth of the World Wide Web (WWW) in terms of the information space and the amount of the users in that space. However, in today'
s world, many systems and approaches make it possible for the users to be guided by the recommendations that they provide about new items such as articles, news, books, music, and movies. However, a lot of traditional recommender systems result in failure when the data to be used throughout the recommendation process is sparse. In another sense, when there exists an inadequate number of items or users in the system, unsuccessful recommendations are produced. Within this thesis work, ReMovender, a web based movie recommendation system, which uses a content boosted collaborative filtering approach, will be presented. ReMovender combines the local/global similarity and missing data prediction v techniques in order to handle the previously mentioned sparseness problem effectively. Besides, by putting the content information of the movies into consideration during the item similarity calculations, the goal of making more successful and realistic predictions is achieved.
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Song, Philip, and André Brogärd. "Performance Analysis of Various Activation Functions Using LSTM Neural Network For Movie Recommendation Systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280451.

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The growth of importance and popularity of recommendations system has increased in many various areas. This thesis focuses on recommendation systems for movies. Recurrent neural networks using LSTM blocks have shown some success for movie recommendation systems. Research has indicated that by changing activation functions in LSTM blocks, the performance, measured as accuracy in predictions, can be improved. In this study we compare four different activation functions (hyperbolic tangent, sigmoid, ELU and SELU activation functions) used in LSTM blocks, and how they impact the prediction accuracy of the neural networks. Specifically, they are applied to the block input and the block output of the LSTM blocks. Our results indicate that the hyperbolic tangent, which is the default, and sigmoid function perform about the same, whereas the ELU and SELU functions perform worse. Further research is needed to identify other activation functions that could improve the prediction accuracy and improve certain aspects of our methodology.
Rekommendationssystem har ökat i betydelse och popularitet i många olika områden. Denna avhandling fokuserar på rekommendationssystem för filmer. Recurrent neurala nätverk med LSTM blocks har visat viss framgång för rekommendationssystem för filmer. Tidigare forskning har indikerat att en ändring av aktiverings funktioner har resulterat i förbättrad prediktering. I denna studie jämför vi fyra olika aktiveringsfunktioner (hyperbolic tangent, sigmoid, ELU and SELU) som appliceras i LSTM blocks och hur de påverkar predikteringen i det neurala nätverket. De appliceras specifikt på block input och block output av LSTM blocken. Våra resultat indikerar att den hyperboliska tangentfunktionen, som är standardvalet, och sigmoid funktionen presterar lika, men ELU och SELU presterar båda sämre. Ytterligare forskning krävs för att indentifiera andra aktiveringsfunktioner och för att förbättra flera delar av metodologin.
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Lokesh, Ashwini. "A Comparative Study of Recommendation Systems." TopSCHOLAR®, 2019. https://digitalcommons.wku.edu/theses/3166.

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Recommendation Systems or Recommender Systems have become widely popular due to surge of information at present time and consumer centric environment. Researchers have looked into a wide range of recommendation systems leveraging a wide range of algorithms. This study investigates three popular recommendation systems in existence, Collaborative Filtering, Content-Based Filtering, and Hybrid recommendation system. The famous MovieLens dataset was utilized for the purpose of this study. The evaluation looked into both quantitative and qualitative aspects of the recommendation systems. We found that from both the perspectives, the hybrid recommendation system performs comparatively better than standalone Collaborative Filtering or Content-Based Filtering recommendation system
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Deirmenci, Hazim. "Enabling Content Discovery in an IPTV System : Using Data from Online Social Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200922.

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Internet Protocol television (IPTV) is a way of delivering television over the Internet, which enables two-way communication between an operator and its users. By using IPTV, users have freedom to choose what content they want to consume and when they want to consume it. For example, users are able to watch TV shows after they have been aired on TV, and they can access content that is not part of any linear TV broadcasts, e.g. movies that are available to rent. This means that, by using IPTV, users can get access to more video content than is possible with the traditional TV distribution formats. However, having more options also means that deciding what to watch becomes more difficult, and it is important that IPTV providers facilitate the process of finding interesting content so that the users find value in using their services. In this thesis, the author investigated how a user’s online social network can be used as a basis for facilitating the discovery of interesting movies in an IPTV environment. The study consisted of two parts, a theoretical and a practical. In the theoretical part, a literature study was carried out in order to obtain knowledge about different recommender system strategies. In addition to the literature study, a number of online social network platforms were identified and empirically studied in order to gain knowledge about what data is possible to gather from them, and how the data can be gathered. In the practical part, a prototype content discovery system, which made use of the gathered data, was designed and built. This was done in order to uncover difficulties that exist with implementing such a system. The study shows that, while it is is possible to gather data from different online social networks, not all of them offer data in a form that is easy to make use of in a content discovery system. Out of the investigated online social networks, Facebook was found to offer data that is the easiest to gather and make use of. The biggest obstacle, from a technical point of view, was found to be the matching of movie titles gathered from the online social network with the movie titles in the database of the IPTV service provider; one reason for this is that movies can have titles in different languages.
Internet Protocol television (IPTV) är ett sätt att leverera tv via Internet, vilket möjliggör tvåvägskommunikation mellan en operatör och dess användare. Genom att använda IPTV har användare friheten att välja vilket innehåll de vill konsumera och när de vill konsumera det. Användare har t.ex. möjlighet att titta på tv program efter att de har sänts på tv, och de kan komma åt innehåll som inte är en del av någon linjär tv-sändning, t.ex. filmer som är tillgängliga att hyra. Detta betyder att användare, genom att använda IPTV, kan få tillgång till mer videoinnhåll än vad som är möjligt med traditionella tv-distributionsformat. Att ha fler valmöjligheter innebär dock även att det blir svårare att bestämma sig för vad man ska titta på, och det är viktigt att IPTV-leverantörer underlättar processen att hitta intressant innehåll så att användarna finner värde i att använda deras tjänster. I detta exjobb undersökte författaren hur en användares sociala nätverk på Internet kan användas som grund för att underlätta upptäckandet av intressanta filmer i en IPTV miljö. Undersökningen bestod av två delar, en teoretisk och en praktisk. I den teoretiska delen genomfördes en litteraturstudie för att få kunskap om olika rekommendationssystemsstrategier. Utöver litteraturstudien identifierades ett antal sociala nätverk på Internet som studerades empiriskt för att få kunskap om vilken data som är möjlig att hämta in från dem och hur datan kan inhämtas. I den praktiska delen utformades och byggdes en prototyp av ett s.k. content discovery system (“system för att upptäcka innehåll”), som använde sig av den insamlade datan. Detta gjordes för att exponera svårigheter som finns med att implementera ett sådant system. Studien visar att, även om det är möjligt att samla in data från olika sociala nätverk på Internet så erbjuder inte alla data i en form som är lätt att använda i ett content discovery system. Av de undersökta sociala nätverkstjänsterna visade det sig att Facebook erbjuder data som är lättast att samla in och använda. Det största hindret, ur ett tekniskt perspektiv, visade sig vara matchningen av filmtitlar som inhämtats från den sociala nätverkstjänsten med filmtitlarna i IPTV-leverantörens databas; en anledning till detta är att filmer kan ha titlar på olika språk.
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Hinas, Toni, and Isabelle Ton. "Recommender Systems for Movie Recommendations." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239376.

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Recommender systems are becoming a large and important market, with commerce moving to the internet and the ability to keep a larger stock of products, one of the biggest hurdles is to organize and show the right product to the right customer. Recommender systems aim at tailoring their products based on their customer need, by predicting how much a user would like a particular product. The recommender systems implemented in this project are within Collaborative filtering (CF) and Content-based filtering (CBF), with a final hybrid system based on combining the systems of CF and CBF. The aim is to evaluate how features such as number of latent factors, regularization factor and learning rate affect prediction accuracy for CF using Matrix factorization and compare the Root-mean square error (RMSE) for the three different systems.Collaborative filtering using matrix factorization resulted in lower RMSE than CBF and the largest factor in lowering error was learning rate. The results did indicate that CBF might perform better than CF when the user-base is small, while also having possibility of somewhat different functionality by recommending products which themselves are similar. The Hybrid recommender system had the lowest RMSE but with insignificant improvements from that of the CF method.
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Books on the topic "Movie recommendation"

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Great Britain. Conveyancing Standard Committee. Getting the money to move: Avoiding completion delays : recommendations of the Conveyancing Standard Committee of the Law Commission. London: Law Commission, 1989.

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De Jong, Bart A., David P. Kroon, and Oliver Schilke. The Future of Organizational Trust Research. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190630782.003.0010.

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This chapter contributes to defining a common research agenda on organizational trust, first by content-analyzing scholarly recommendations for future research published between 2007 and 2011 across 347 articles and 58 social science journals and second by reviewing the latest developments in trust research published between 2012 and 2015 across 111 articles and 31 top-tier management journals. This content analysis of scholarly recommendations yields an emergent organizing framework that offers systematic insight into the trust community’s beliefs about how the field should move forward, while the review of the latest developments in the field provide insight into whether these recommendations have recently been followed up on, or whether research has developed in previously unanticipated directions. The chapter concludes with suggestions on how individual researchers and the trust community as a whole can use and build on these findings to help advance the field.
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Bergeron, Diane M., Chantal van Esch, and Phillip S. Thompson. Citizenship Behavior and Objective Career Outcomes: A Review and Agenda for Future Work. Edited by Philip M. Podsakoff, Scott B. Mackenzie, and Nathan P. Podsakoff. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190219000.013.9.

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A small but growing body of research on organizational citizenship behavior (OCB) and objective career outcomes highlights the need for more consistency across studies. This chapter critically examines extant literature and highlights key issues in current research. More specifically, we identify three main issues in the current literature. First, many OCB studies do not include a corollary measure of task behavior. Second, there seems to be an assumption that performance evaluations are positively related to objective career outcomes. Third, it is important to acknowledge that studying reward recommendations is not the same as studying actual rewards. Following discussion of these three issues, we then review the studies on OCB and various objective career outcomes (i.e., productivity, salary and financial rewards, other rewards, promotion and career advancement); point out patterns and trends across the studies; and make recommendations for how the field can move forward in terms of future research directions.
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Young, Susan M. Financial Analysts. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190269999.003.0007.

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Financial analysts are important players in the marketplace. Analysts’ reports, which include forecasts of earnings and stock recommendations, move market prices. Investors, both large and small, rely on the information in reports when forming their investment decisions. Given the relevance of financial analysts’ research, understanding whether their reports are biased is important. Despite an increase in market regulation, evidence suggests that analysts’ reports are biased. Research also finds that analysts’ bias increases when information uncertainty is high. Thus, investors should understand the possible dangers in blindly relying on research by financial analysts.
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Balanzá-Martínez, Vicent, Sofia Brissos, Maria Lacruz, and Rafael Tabarés-Seisdedos. Pharmacotherapy of bipolar disorder: impact on neurocognition. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198748625.003.0025.

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Neurocognitive dysfunction is a core feature of bipolar disorder (BD), which may be further compounded by several clinical factors, such as medications. There is growing interest on the potential impact of pharmacotherapy (lithium, anticonvulsants, antipsychotics, and other) on neurocognition. This chapter summarizes a critical, descriptive update of the literature, mostly focused on human data. Based on current studies, medication-associated neurocognitive side effects cannot be clearly distinguished from those intrinsic to BD. Moreover, available research is limited by several methodological flaws. We suggest some likely profitable directions to move the field forward, as well as several recommendations to manage cognitive deficits in clinical practice. The neurocognitive impact of medications used to treat BD clearly warrants further, higher-quality research.
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Vandenberg, Martina. Peacekeeping, Human Trafficking, and Sexual Abuse and Exploitation. Edited by Fionnuala Ní Aoláin, Naomi Cahn, Dina Francesca Haynes, and Nahla Valji. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199300983.013.32.

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This chapter provides an overview of human trafficking and other forms of sexual abuse committed by peacekeepers and civilians employed in peacekeeping missions. It opens with a historical review of violations committed by peacekeepers and the current international response to the issue. The chapter introduces relevant international legal instruments, including the UN Protocol to Suppress, Prevent and Punish Trafficking in Persons, and examines the United Nations’ response to various instances of misconduct. Focusing on Bosnia and Herzegovina and the MINUSCA mission in the Central African Republic, the chapter details the consistent failure of national courts to prosecute offenders and the inability of the UN to take action beyond repatriating the offenders. The chapter closes with recommendations for the UN to move beyond prevention work to improve enforcement of peacekeeper conduct policies.
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Goldenberg, Don. COVID's Impact on Health and Healthcare Workers. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780197575390.001.0001.

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The symptoms, risk factors and typical course of mild, moderate and severe COVID-19 infections are detailed, focusing on correlations with hospitalization and death. The physical and emotional toll on healthcare workers is described, as well as the innovations and sacrifices made by physicians, nurses, and hospitals during the pandemic. Present and enduring changes in primary care and mental healthcare, including increased utilization of telemedicine, are explained. The misinformation and disinformation raging during the pandemic and their adverse effect on public health and patient recovery are uncovered. There is a focus on persistent symptoms, long after the initial COVID infection, including long-COVID syndrome. The book concludes with recommendations to best move forward, addressing public health, healthcare inequities, long-term care facilities, primary care, healthcare worker well-being, and following science and truth.
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Burrus, Jeremy, Krista Mattern, Bobby D. Naemi, and Richard D. Roberts. Building Better Students. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199373222.001.0001.

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The face of the workforce is rapidly changing. Technological advances mean that jobs previously serving as major drivers of the world’s economy are now fully automated. Furthermore, the automatization of many common work activities means that those currently entering the workforce require a different set of skills than those entering the workforce of the 20th century. As such, there is a need to redefine what it means to be “ready to work.” This has led to a major reboot, with new research, applied, and policy questions: How do we define and measure work readiness? How should we prepare students for the workforce? And how can we bridge gaps between college and workforce readiness? A key to reconsidering workforce readiness is placing greater emphasis on measuring and developing noncognitive or “21st century” skills, such as teamwork, creativity, and persistence, and focusing more attention on fostering activities that engage, prepare, and advance students for the future. This volume brings together some of the world’s cutting-edge workforce readiness researchers from the fields of industrial/organizational, educational, and personality psychology to tackle these disparate issues. It concludes with a summary of what has been learned and a set of recommendations for educators, researchers, and policymakers to move the field forward. These recommendations represent a crucial first step to “building better students,” who will truly be ready to work.
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Doucerain, Marina, Norman Segalowitz, and Andrew G. Ryder. Acculturation Measurement. Edited by Seth J. Schwartz and Jennifer Unger. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190215217.013.7.

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This article discusses the importance of clear and precise conceptualizations of acculturation as well as the need for consistencies in definition, operationalization, and measurement. More specifically, it argues for an expanded acculturation research toolkit that does not rely too heavily on self-report acculturation scales. The article begins with an overview of the state of affairs with respect to acculturation conceptualizations and methods, paying particular attention to the unidimensional, bidimensional, and multidimensional frameworks of psychological acculturation. It then considers ways in which commonly used definitions and methods of acculturation can be used more intelligently. It also describes alternative methods for researchers interested in moving beyond self-report rating scales, a tiered approach to acculturation research, and method-specific health considerations. Finally, it offers some recommendations aimed at helping the field of acculturation and health research move forward.
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Jack, Gavin. Advancing Postcolonial Approaches in Critical Diversity Studies. Edited by Regine Bendl, Inge Bleijenbergh, Elina Henttonen, and Albert J. Mills. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199679805.013.3.

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Postcolonialism provides theoretical resources that speak well to the concerns of critical diversity scholars, notably the interest in culture, power, and the construction of (human) differences. Yet, with notable exceptions, there is a paucity of research on workplace diversity underpinned by postcolonialism. This chapter seeks to animate and advance postcolonial scholarship in critical diversity studies, and responds to calls to revitalize this scholarly sub-field. Based on a review of critical diversity studies (including the few that have used postcolonial perspectives), two recommendations are made to advance postcolonial critiques. First, critical diversity scholars might undertake a closer engagement with psychoanalytic and discursive variants of postcolonial theory to generate complex understandings of the psychological dimensions of (post)colonial subjectivities and the persistence of racism in organizations. Second, scholars might also consider the merits of ‘Southern Theory’ in order to move beyond the noted Eurocentric limits of existing gender and diversity research.
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Book chapters on the topic "Movie recommendation"

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Lekakos, George, Matina Charami, and Petros Caravelas. "Personalized Movie Recommendation." In Handbook of Multimedia for Digital Entertainment and Arts, 3–26. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-89024-1_1.

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Rajarajeswari, S., Sharat Naik, Shagun Srikant, M. K. Sai Prakash, and Prarthana Uday. "Movie Recommendation System." In Emerging Research in Computing, Information, Communication and Applications, 329–40. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5953-8_28.

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Tang, Song, Zhiyong Wu, and Kang Chen. "Movie Recommendation via BLSTM." In MultiMedia Modeling, 269–79. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51814-5_23.

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Ko, Sang-Ki, Sang-Min Choi, Hae-Sung Eom, Jeong-Won Cha, Hyunchul Cho, Laehyum Kim, and Yo-Sub Han. "A Smart Movie Recommendation System." In Lecture Notes in Computer Science, 558–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21793-7_63.

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Padhi, Ashis Kumar, Ayog Mohanty, and Sipra Sahoo. "FindMoviez: A Movie Recommendation System." In Intelligent Systems, 49–57. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6081-5_5.

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Farinella, Tania, Sonia Bergamaschi, and Laura Po. "A Non-intrusive Movie Recommendation System." In On the Move to Meaningful Internet Systems: OTM 2012, 736–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33615-7_19.

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Jain, Kartik Narendra, Vikrant Kumar, Praveen Kumar, and Tanupriya Choudhury. "Movie Recommendation System: Hybrid Information Filtering System." In Intelligent Computing and Information and Communication, 677–86. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7245-1_66.

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Lee, Maria R., Tsung Teng Chen, and Ying Shun Cai. "Amalgamating Social Media Data and Movie Recommendation." In Knowledge Management and Acquisition for Intelligent Systems, 141–52. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42706-5_11.

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Saraswat, Mala, and Shampa Chakraverty. "Leveraging Movie Recommendation Using Fuzzy Emotion Features." In Data Science and Analytics, 475–83. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8527-7_40.

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Ahn, Shinhyun, and Chung-Kon Shi. "Exploring Movie Recommendation System Using Cultural Metadata." In Transactions on Edutainment II, 119–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03270-7_9.

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Conference papers on the topic "Movie recommendation"

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Subramaniam, Rajan, Roger Lee, and Tokuro Matsuo. "Movie Master: Hybrid Movie Recommendation." In 2017 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2017. http://dx.doi.org/10.1109/csci.2017.56.

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Liu, Anan, Yongdong Zhang, and Jintao Li. "Personalized movie recommendation." In the seventeen ACM international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1631272.1631429.

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Sharma, Nisha, and Mala Dutta. "Movie Recommendation Systems." In ICCCM'20: 2020 The 8th International Conference on Computer and Communications Management. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3411174.3411194.

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Halder, Sajal, A. M. Jehad Sarkar, and Young-Koo Lee. "Movie Recommendation System Based on Movie Swarm." In 2012 International Conference on Cloud and Green Computing (CGC). IEEE, 2012. http://dx.doi.org/10.1109/cgc.2012.121.

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Halder, Sajal, Md Samiullah, A. M. Jehad Sarkar, and Young-Koo Lee. "Movie swarm: Information mining technique for movie recommendation system." In 2012 7th International Conference on Electrical & Computer Engineering (ICECE). IEEE, 2012. http://dx.doi.org/10.1109/icece.2012.6471587.

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Pathak, Dharmendra, S. Matharia, and C. N. S. Murthy. "ORBIT: Hybrid movie recommendation engine." In 2013 International Conference on Emerging Trends in Computing, Communication and Nanotechnology (ICE-CCN). IEEE, 2013. http://dx.doi.org/10.1109/ice-ccn.2013.6528589.

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Nie, Dong, Lingzi Hong, and Tingshao Zhu. "Movie Recommendation Using Unrated Data." In 2013 12th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2013. http://dx.doi.org/10.1109/icmla.2013.70.

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Xu, Zhe, and Ya Zhang. "Automatic generated recommendation for movie trailers." In 2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE, 2013. http://dx.doi.org/10.1109/bmsb.2013.6621738.

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Kapoor, Nimish, Saurav Vishal, and Krishnaveni K. S. "Movie Recommendation System Using NLP Tools." In 2020 5th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2020. http://dx.doi.org/10.1109/icces48766.2020.9137993.

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Maheshwari, Ankit, Anuradha Kumari, Anjali Kumari, Neeraj Kumar, and Nandini B M. "Movie Recommendation System using Apache Spark." In 3rd National Conference on Image Processing, Computing, Communication, Networking and Data Analytics. AIJR Publisher, 2018. http://dx.doi.org/10.21467/proceedings.1.45.

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Reports on the topic "Movie recommendation"

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Golbeck, Jennifer. Generating Predictive Movie Recommendations from Trust in Social Networks. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada447900.

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Aiginger, Karl, Andreas Reinstaller, Michael Böheim, Rahel Falk, Michael Peneder, Susanne Sieber, Jürgen Janger, et al. Evaluation of Government Funding in RTDI from a Systems Perspective in Austria. Synthesis Report. WIFO, Austria, August 2009. http://dx.doi.org/10.22163/fteval.2009.504.

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In the spring of 2008, WIFO, KMU Forschung Austria, Prognos AG in Germany and convelop were jointly commissioned by the Austrian Federal Ministry for Transport, Innovation and Technology and the Austrian Federal Ministry of Economy, Family and Youth to perform a systems evaluation of the country's research promotion and funding activities. Based on their findings, six recommendations were developed for a change in Austrian RTDI policy as outlined below: 1. to move from a narrow to a broader approach in RTDI policy (links to education policy, consideration of the framework for innovation such as competition, international perspectives and mobility); 2. to move from an imitation to a frontrunner strategy (striving for excellence and market leadership in niche and high-quality segments, increasing market shares in advanced sectors and technology fields, and operating in segments of relevance for society); 3. to move from a fragmented approach to public intervention to a more coordinated and consistent approach(explicit economic goals, internal and external challenges and reasoning for public intervention); 4. to move from a multiplicity of narrowly defined funding programmes to a flexible, dynamic policy that uses a broader definition of its tasks and priorities (key technology and research segments as priority-action fields, adequate financing of clusters and centres of excellence); 5. to move from an unclear to a precisely defined allocation of responsibilities between ministries and other players in the field (high-ranking steering group at government level, monitoring by a Science, Research and Innovation Council); 6. to move from red-tape-bound to a modern management of public intervention (institutional separation between ministries formulating policies and agencies executing them, e.g., by "progressive autonomy").
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Sowa, Patience, Rachel Jordan, Wendi Ralaingita, and Benjamin Piper. Higher Grounds: Practical Guidelines for Forging Learning Pathways in Upper Primary Education. RTI Press, May 2021. http://dx.doi.org/10.3768/rtipress.2021.op.0069.2105.

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To address chronically low primary school completion rates and the disconnect between learners’ skills at the end of primary school and the skills learners need to thrive in secondary school identified in many low- and middle-income countries, more investment is needed to improve the quality of teaching and learning in upper primary grades. Accordingly, we provide guidelines for improving five components of upper primary education: (1) In-service teacher professional development and pre-service preparation to improve and enhance teacher quality; (2) a focus on mathematics, literacy, and core content-area subjects; (3) assessment for learning; (4) high-quality teaching and learning materials; and (5) positive school climates. We provide foundational guiding principles and recommendations for intervention design and implementation for each component. Additionally, we discuss and propose how to structure and design pre-service teacher preparation and in-service teacher training and ongoing support, fortified by materials design and assessment, to help teachers determine where learners are in developmental progressions, move learners towards mastery, and differentiate and support learners who have fallen behind. We provide additional suggestions for integrating a whole-school climate curriculum, social-emotional learning, and school-related gender-based violence prevention strategies to address the internal and societal changes learners often face as they enter upper primary.
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Coulson, Saskia, Melanie Woods, Drew Hemment, and Michelle Scott. Report and Assessment of Impact and Policy Outcomes Using Community Level Indicators: H2020 Making Sense Report. University of Dundee, 2017. http://dx.doi.org/10.20933/100001192.

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Making Sense is a European Commission H2020 funded project which aims at supporting participatory sensing initiatives that address environmental challenges in areas such as noise and air pollution. The development of Making Sense was informed by previous research on a crowdfunded open source platform for environmental sensing, SmartCitizen.me, developed at the Fab Lab Barcelona. Insights from this research identified several deterrents for a wider uptake of participatory sensing initiatives due to social and technical matters. For example, the participants struggled with the lack of social interactions, a lack of consensus and shared purpose amongst the group, and a limited understanding of the relevance the data had in their daily lives (Balestrini et al., 2014; Balestrini et al., 2015). As such, Making Sense seeks to explore if open source hardware, open source software and and open design can be used to enhance data literacy and maker practices in participatory sensing. Further to this, Making Sense tests methodologies aimed at empowering individuals and communities through developing a greater understanding of their environments and by supporting a culture of grassroot initiatives for action and change. To do this, Making Sense identified a need to underpin sensing with community building activities and develop strategies to inform and enable those participating in data collection with appropriate tools and skills. As Fetterman, Kaftarian and Wanderman (1996) state, citizens are empowered when they understand evaluation and connect it in a way that it has relevance to their lives. Therefore, this report examines the role that these activities have in participatory sensing. Specifically, we discuss the opportunities and challenges in using the concept of Community Level Indicators (CLIs), which are measurable and objective sources of information gathered to complement sensor data. We describe how CLIs are used to develop a more indepth understanding of the environmental problem at hand, and to record, monitor and evaluate the progress of change during initiatives. We propose that CLIs provide one way to move participatory sensing beyond a primarily technological practice and towards a social and environmental practice. This is achieved through an increased focus in the participants’ interests and concerns, and with an emphasis on collective problem solving and action. We position our claims against the following four challenge areas in participatory sensing: 1) generating and communicating information and understanding (c.f. Loreto, 2017), 2) analysing and finding relevance in data (c.f. Becker et al., 2013), 3) building community around participatory sensing (c.f. Fraser et al., 2005), and 4) achieving or monitoring change and impact (c.f. Cheadle et al., 2000). We discuss how the use of CLIs can tend to these challenges. Furthermore, we report and assess six ways in which CLIs can address these challenges and thereby support participatory sensing initiatives: i. Accountability ii. Community assessment iii. Short-term evaluation iv. Long-term evaluation v. Policy change vi. Capability The report then returns to the challenge areas and reflects on the learnings and recommendations that are gleaned from three Making Sense case studies. Afterwhich, there is an exposition of approaches and tools developed by Making Sense for the purposes of advancing participatory sensing in this way. Lastly, the authors speak to some of the policy outcomes that have been realised as a result of this research.
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HEFNER, Robert. IHSAN ETHICS AND POLITICAL REVITALIZATION Appreciating Muqtedar Khan’s Islam and Good Governance. IIIT, October 2020. http://dx.doi.org/10.47816/01.001.20.

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Ours is an age of pervasive political turbulence, and the scale of the challenge requires new thinking on politics as well as public ethics for our world. In Western countries, the specter of Islamophobia, alt-right populism, along with racialized violence has shaken public confidence in long-secure assumptions rooted in democracy, diversity, and citizenship. The tragic denouement of so many of the Arab uprisings together with the ascendance of apocalyptic extremists like Daesh and Boko Haram have caused an even greater sense of alarm in large parts of the Muslim-majority world. It is against this backdrop that M.A. Muqtedar Khan has written a book of breathtaking range and ethical beauty. The author explores the history and sociology of the Muslim world, both classic and contemporary. He does so, however, not merely to chronicle the phases of its development, but to explore just why the message of compassion, mercy, and ethical beauty so prominent in the Quran and Sunna of the Prophet came over time to be displaced by a narrow legalism that emphasized jurisprudence, punishment, and social control. In the modern era, Western Orientalists and Islamists alike have pushed the juridification and interpretive reification of Islamic ethical traditions even further. Each group has asserted that the essence of Islam lies in jurisprudence (fiqh), and both have tended to imagine this legal heritage on the model of Western positive law, according to which law is authorized, codified, and enforced by a leviathan state. “Reification of Shariah and equating of Islam and Shariah has a rather emaciating effect on Islam,” Khan rightly argues. It leads its proponents to overlook “the depth and heights of Islamic faith, mysticism, philosophy or even emotions such as divine love (Muhabba)” (13). As the sociologist of Islamic law, Sami Zubaida, has similarly observed, in all these developments one sees evidence, not of a traditionalist reassertion of Muslim values, but a “triumph of Western models” of religion and state (Zubaida 2003:135). To counteract these impoverishing trends, Khan presents a far-reaching analysis that “seeks to move away from the now failed vision of Islamic states without demanding radical secularization” (2). He does so by positioning himself squarely within the ethical and mystical legacy of the Qur’an and traditions of the Prophet. As the book’s title makes clear, the key to this effort of religious recovery is “the cosmology of Ihsan and the worldview of Al-Tasawwuf, the science of Islamic mysticism” (1-2). For Islamist activists whose models of Islam have more to do with contemporary identity politics than a deep reading of Islamic traditions, Khan’s foregrounding of Ihsan may seem unfamiliar or baffling. But one of the many achievements of this book is the skill with which it plumbs the depth of scripture, classical commentaries, and tasawwuf practices to recover and confirm the ethic that lies at their heart. “The Quran promises that God is with those who do beautiful things,” the author reminds us (Khan 2019:1). The concept of Ihsan appears 191 times in 175 verses in the Quran (110). The concept is given its richest elaboration, Khan explains, in the famous hadith of the Angel Gabriel. This tradition recounts that when Gabriel appeared before the Prophet he asked, “What is Ihsan?” Both Gabriel’s question and the Prophet’s response make clear that Ihsan is an ideal at the center of the Qur’an and Sunna of the Prophet, and that it enjoins “perfection, goodness, to better, to do beautiful things and to do righteous deeds” (3). It is this cosmological ethic that Khan argues must be restored and implemented “to develop a political philosophy … that emphasizes love over law” (2). In its expansive exploration of Islamic ethics and civilization, Khan’s Islam and Good Governance will remind some readers of the late Shahab Ahmed’s remarkable book, What is Islam? The Importance of Being Islamic (Ahmed 2016). Both are works of impressive range and spiritual depth. But whereas Ahmed stood in the humanities wing of Islamic studies, Khan is an intellectual polymath who moves easily across the Islamic sciences, social theory, and comparative politics. He brings the full weight of his effort to conclusion with policy recommendations for how “to combine Sufism with political theory” (6), and to do so in a way that recommends specific “Islamic principles that encourage good governance, and politics in pursuit of goodness” (8).
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Digital Health Implementation Guide for the Pacific. Asian Development Bank, June 2021. http://dx.doi.org/10.22617/tim210178-2.

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Planning and investing in digital health information systems can lead to improvements in decision-making, patient services, and quality of care. With increased internet connectivity, Pacific island countries have more opportunities to move away from paper-based information systems and connect remote health facilities for greater information exchange. This guide provides resources for those working in health information planning, design, and implementation, including in public health, and includes examples from across the Pacific. The guide makes recommendations on how to achieve a comprehensive and sustainable digital health information system that improves decision-making and, ultimately, improves patient experiences.
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