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Dissertations / Theses on the topic 'Movie recommendation'
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1
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.
2
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.
4
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 &ldquoWhich 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.
6
Ozbal, Gozde. "A Content Boosted Collaborative Filtering Approach For Movie Recommendation Based On Local &." 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.
7
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.
8
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
9
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.
10
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.
11
Ivarsson, Jakob, and Mathias Lindgren. "Movie recommendations using matrix factorization." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186400.
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A recommender system is a tool for recommending personalized content for users based on previous behaviour. This thesis examines the impact of considering item and user bias in matrix factorization for implementing recommender systems. Previous work have shown that user bias have an impact on the predicting power of a recommender system. In this study two different implementations of matrix factorization using stochastic gradient descent are applied to the MovieLens 10M dataset to extract latent features, one of which takes movie and user bias into consideration. The algorithms performed similarly when looking at the prediction capabilities. When examining the features extracted from the two algorithms there was a strong correlation between extracted features and movie genres. We show that each feature form a distinct category of movies where each movie is represented as a combination of the categories. We also show how features can be used to recommend similar movies.
12
Rydholm, Gustav. "Recommendation System for Movies." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200600.
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Kuroptev, Roman, and Anton Lagerlöf. "Improving movie recommendations through social media matching." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20834.
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Rekommendationssystem är idag väsentliga för att navigera den enorma mängd produkter tillgängliga via internet. Då social media i form av Twitter vid tidigare tillfällen använts för att generera filmrekommendationer har detta främst varit för att hantera cold-start, ett vanligt drabbande problem för collaborative-filtering. I detta arbete adresseras istället hur top-k rekommendationer påverkas vid integrering av social media data i rekommendationssystemet. För att svara på denna fråga har en prototyp av nytt slag utvecklats inom processmodellen för Design Science. Systemet rankar om top-k rekommendationer baserat på resultatet av social matchning där användares Tweets matchas med nyckelord för filmer genom latent semantic indexing (LSI) similarity. Prototypen evalueras genom experiment som adresserar funktionalitet, noggrannhet, konsekvens och prestanda. Resultatet visar att mätetalen NDCG och MAP för top-k rekommendationer förbättras med social matching jämfört med att enbart använda collaborative filtering.Recommender systems are a crucial part of navigating the vast number of products on the internet. Social media, in the form of Twitter microblogs, has been previously used to produce movie recommendations, yet this has mainly been to solve cold-start, a common problem in collaborative filtering environments. This work addresses how top-k recommendations in a collaborative filtering environment are affected when augmented with social media data. To answer this question a novel prototype is developed following a design science process model. This system re-ranks top-k recommendations based on a social matching process where Tweets are matched with movie keywords through latent semantic indexing (LSI) similarity. The prototype is evaluated through experiments regarding functionality, accuracy, consistency, and performance. The results show that NDCG and MAP metrics of the top-k recommendations improve with social matching compared to only using the collaborative filtering algorithms.
14
Mejdi, Sami, and Camjar Djoweini. "Applying Parameter Priority for User Recommendations Concerning Movies and Documentaries : User Behavior Prediction." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186455.
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An area where algorithms are used in order to optimize prediction of what users prefer in di↵erent market areas. By modifying algorithms that has already been made, one can improve the accuracy of the predictions made. The purpose of this study was to find out if existing behavioral prediction algorithms can be improved by taking certain parameters into consideration. This was achieved by conducting a survey where users could vote on what parameters they take into consideration when chosing a movie to watch. Once having the important parameters, a modification was made to an existing algorithm so that a comparison in di↵erences was possible. The results were clear and indicated that the modification was very data-reliant and therefore not optimal for every situation.
15
FLORÊNCIO, João Carlos Procópio. "Análise e predição de bilheterias de filmes." Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/17639.
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Prever o sucesso de um filme e, por consequência, seu sucesso nas bilheterias tem uma grande importância na indústria cinematográfica, desde a fase de pré-produção do filme, quando os investidores querem saber quais serão os filmes mais promissores, até nas semanas seguintes ao seu lançamento, quando se deseja prever as bilheterias das semanas restantes de exibição. Por conta disso, essa área tem sido alvo de muitos estudos que tem usado diferentes abordagens de predição, seja na seleção das características dos filmes como nas técnicas de aprendizagem, para atingir uma maior capacidade de prever o sucesso dos filmes. Neste trabalho de mestrado, foi feita uma investigação sobre o comportamento das principais características dos filmes (gênero, classificação etária, orçamento de produção, etc), com maior foco nos resultados das bilheterias e sua relação com as características dos filmes, de forma a obter uma visão mais clara de como as caracaterísticas dos filmes podem influenciar no seu sucesso, seja ele interpretado como lucro ou volume de bilheterias. Em seguida, em posse de uma base de filmes extraída do Box-Office Mojo e do IMDb, foi proposto um novo modelo de predição de box office utilizando os dados disponíveis dessa base, que é composta de: meta-dados dos filmes, palavras-chaves, e dados de bilheterias. Algumas dessas características são hibridizadas com o objetivo evidenciar as combinações de características mais importantes. É aplicado também um processo de seleção de características para excluir aquelas que não são relevantes ao modelo. O modelo utiliza Random Forest como máquina de aprendizagem. Os resultados obtidos com a técnica proposta sugerem, além de uma maior simplificação do modelo em relação a estudos anteriores, que o método consegue obter taxas de acerto superior 90% quando a classificação é medida com a métrica 1-away (quando a amostra é classificada com até 1 classe de distância), e consegue melhorar a qualidade da predição em relação a estudos anteriores quando testado com os dados da base disponível.
Predicting the success of a movie and, consequently, its box office success, has a huge importance in the motion pictures industry. Its importance comes since from the pre-production period, when the investors want to know the most promising movies to invest, until the first few weeks after release, when exhibitors want to predict the box office of the remaining weeks of exhibition. As result, this area has been subject of many studies which have used different prediction approaches, in both feature selection and learning methods, to achieve better capacity to predict movies’ success. In this mastership work, a deep research about the movie’s main features (genre, MPAA, production budget, etc) has been done, with more focus on the results of box offices and its relation with the movie’s features in order to get a clearer view of the organization of information and how variables can influence the success of a film, whether this success be interpreted as profit or revenue volumes at the box office. Then, in possession of a movie database extracted from Box-Office Mojo and IMDb, it was proposed a new box office prediction model based on available data from the database composed of: movie meta-data, key-words and box office data. Some of these features are hybridized aiming to emphasize the most important features’ combinations. A features’ selection process is also applied to exclude irrelevant features. The obtained results with the proposed method suggests, besides a further simplification of the model compared to previous studies, that the method can get hit rate of more than 90% when classification is measured with the metric 1-away (when the sample is classified within 1 class of distance from the right class), and achieve a improvement in the prediction quality when compared to previous studies using the available database.
16
LIN, SHIH-FENG, and 林士峰. "Movie Recommendation System Based on IMDb." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/4b5uw9.
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碩士逢甲大學
資訊工程學系
107
In the era of information explosion, it is easy for us to misunderstand many wrong information because of social media reports. In view of this, this article wants to explore how to apply the programming in information engineering to everyday life and to be relevant to everyone. The issue or the matter, and thus improve people's life today. This paper will provide an improved analysis of the recommendations of the Yahoo movie review. Since the statistical scores of the Yahoo movie Critics or other film reviews are not necessarily able to recommend your favorite movies, some of the film scores may even have doubts about irrigation or irrelevant film content evaluation. If we can analyze whether a movie is objectively Selling, for example, from the type of film, the budget of the movie, the card...etc., plus the evaluation as the analysis data, by changing the weight and discussing the relationship between the parameters to predict whether the movie will make the audience love and pay the bill, The focus of this paper is to explore. This paper will use the 5000 IMDb movie dataset. This site offers a variety of and is a credible public network data set. We will try to find a suitable movie with different parameters with this 5000 IMDb movie dataset. This paper will construct a recommendation system based on popularity and dataset content. In order to achieve this goal, first, we first do data pre-processing on the IMDb data set, and remove irrelevant information and missing values. Then, we compare the remaining data for similarity and use KNN nearest neighbors. The algorithm recommends five movies; finally, we adjust the weight ratio of the parameters to find the best recommendation. We got the best classification results, understood the relationship between the parameters, and knew the best recommendation algorithm.
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Garat, Fernanda Velasco. "Presentation Bias in movie recommendation algorithms." Master's thesis, 2021. http://hdl.handle.net/10362/114353.
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Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization Information Analysis and ManagementThe emergence of video on demand (VOD) has transformed the way the content finds its audience. Several improvements have been made on algorithms to provide better movie recommendations to individuals. Given the huge variety of elements that characterize a film (such as casting, genre, soundtrack, amongst others artistic and technical aspects) and that characterize individuals, most of the improvements relied on accomplishing those characteristics to do a better job regarding matching potential clients to each product. However, little attention has been given to evaluate how the algorithms’ result selection are affected by presentation bias. Understanding bias is key to choosing which algorithms will be used by the companies. The existence of a system with presentation bias and feedback loop is already a problem stated by Netflix. In this sense, this research will fill that gap providing a comparative analysis of the bias of the major movie recommendation algorithms.
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Fatih, Ghilman, and 吉雷曼. "MOVIE SALES PATTERN CLUSTERING FOR RECOMMENDATION SYSTEM." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/63006215275454988404.
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碩士國立臺灣科技大學
工業管理系
103
A recommender system (RS) where consumers are presented with items that are relevant to them obtains a lot of attention on e-commerce. By utilizing consumer’s explicit feedback given to the system, recommendation given can be more accurate. The mathematics behind RS is using a matrix sized number of users multiplies number of items available. Calculating this very big matrix is exhaustive and inefficient. In this research, the concept of divide-and-conquer were borrowed by clustering items into several groups for enhancing the matrix computation in RS. Twitter’s user movie rating data was used to generate the matrix and IMDb movie data was used for clustering the movies. Two-step clustering was proposed to first cluster the movies based on its internal attributes. The second step is clustering movies by sales pattern of each movie. When clustering movies by sales pattern, the duration of a movie shown in theater can be considered as a product life. For better clustering time-series sales pattern, the discrete sales information was transformed into functional data. The functional data clustering was performed and the accuracy, computation time and recommendation given by traditional RS and our pre-cluster RS are compared. We found by clustering the items before doing matrix factorization, the accuracy of the predicted rating is better and computation time is faster. Moreover, the recommendation given is also based on the combination of latent features and items similarity.
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Lin, Chung-Yu, and 林重佑. "A Study on LVQ Based Switching Hybrid Movie Recommendation." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/11873132375795364614.
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碩士國立雲林科技大學
資訊管理系碩士班
100
The great development of Internet technology brings more and more people to use computers to extract abundant content from this platform by their high speed computing ability. To keep the most valued consumers, most corporations have launched to the electronic environment in order to provide personalized services to their consumers. Content-based filtering and collaborative filtering are widely used techniques in recommendation system. The former method analyzes used records from users to make recommendation. The latter one takes the advantage of user preferences to recommend suitable products. Although they can offer proper recommendations, some shortcomings are existed individually. Thus, the hybrid recommendation technique combines the above advantages to recommend content corresponded with users’ requirements. Recently, hybrid recommendation technique is affected by neural network’s learning ability. A lot of supervised neural networks are combined with hybrid recommendation. Previous studies adopted three layers or multiple layers to construct recommendation. Their drawbacks are slow convergence and hard to design. In this paper, we present a novel switching hybrid recommendation framework based on Learning Vector Quantization (LVQ) and collaborative filtering to provide personalized recommendation. Our approach applies the two-layer architecture in LVQ and collaborative filtering to build switching hybrid recommendation. MovieLens data set is used to test our framework. Results show that switching hybrid strategy provides promising personalized recommendation. Our experiment gains 79% of precision, and the recall rate also reaches 82%.
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Yang, Chun-Yuan, and 楊鈞元. "A Novel NMF-Based Movie Recommendation with Time Decay." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/j97ww6.
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碩士國立中央大學
資訊管理學系
107
One of the most popular approaches to handle very large datasets is matrix factorization(MF) technique. The MF method was commonly used in recommendation systems due to the precise prediction of the user’s interest. Especially one of the successful method Non-Negative Matrix Factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. NMF-based techniques, however, could not properly capture time influences on user preferences. In this paper, by considering time impacts on preferences, we propose two novel NMF-based recommendation system, Dec_NMF, to consider user preferences over time. Our proposed method extends the concept of the change of human interest through time to capture user’s current preference and reduce impacts which was rated from a long time ago. We adjust the rating using three different linear and three different non-linear time decay. Each function represents different decay degree of preferences to simulate the human’s interest behavior. The experimental results show that proposed methods outperforms the traditional MF and NMF model. Furthermore, we apply Dec_NMF on MovieLens datasets to demonstrate the effectiveness of Dec_NMF recommendation.
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Wang, Chun-Yuan, and 王群元. "A Recommendation System based on Ontology Technologies and Social Networks – An Example of Movie Recommendation." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/94233173902962852217.
Full textAbstract:
碩士國立高雄應用科技大學
資訊管理研究所碩士班
96
With the rapid development of Internet, information on the Internet is more diversified, and the size of information on the Internet grows quickly. As a result, users have to spend more time to search information they need. Recommendation system provides personalized services and filtering mechanisms that assist users to filter unsuitable information according to users’ individual preferences. However, there exists the sparsity problem in traditional recommendation system. Social networks can solve the sparse problem for recommendation system by using trust mechanism. However, it only can build a trust mechanism between people without any information about individual preferences. This study presents a system architecture that integrates social networks with ontology technologies for improving the precision of movie recommendation. The establishment of movie ontology enables our recommendation system to identify precisely users’ preferences in movie and improves the overall performance of the recommendation system.
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Hung, Wei-Han, and 洪瑋含. "Movie Recommendation Service using Item-based Collaborative Filtering on Hadoop." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/72197243784490019470.
Full textAbstract:
碩士靜宜大學
資訊工程學系
103
The information and network technologies have changed people’s lifestyles and purchasing behavior. By Internet growth, recommender systems can provide various suggestion applications. Most past researches mainly have aimed at establishing content-based recommendation services. These services calculate user interest according to their historical browsing and consumption behavior for provide personalized products. This kind of systems has some disadvantages, while comparing with collaborative filtering recommender algorithms. Collaborative filtering recommendation mechanisms suggest user the interesting information through the experience of the others with the same preference. This thesis establishes a personalized movie recommendation system. The system adopts few collaborative filtering recommender algorithms with external linked data free available on Internet. Parts of the linked data from Douban movie and Movielens are organized in NoSQL database. The movie rating data is as the dataset for performing collaborative filtering recommender using Mahout. The system provides the method to obtain these data for the external developer. A prototype system proposed in the thesis suggests users movies that they interested, and has a mechanism to gain user feedback. Finally, the system applies the feedback information to the three kinds of similarity algorithms, Euclidean Distance, Maximum likelihood, and Jaccard similarity coefficient. The two metrics usually in data mining systems, Recall Rate and Precision Rate, are adopted for algorithm comparison. The system also evaluates the three algorithms by F1-Measurecombining the rate metrics.
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WU, TING-YING, and 吳亭瑩. "A Social Community Mining and Sentiment Analysis Combined Recommendation for Movie." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/duqyfx.
Full textAbstract:
碩士國立臺北科技大學
資訊與財金管理系
107
With the vigorous development of the Internet in recent years, people nowadays regard the Internet as an important tool for social networking and information exchange. As a result, a huge amount of information has been accumulated on the Internet, which also contains a lot of unrelated information. In order to help consumers quickly find the information and products they need, the recommendation system appeared on the scene. Movie recommendation has always been a popular research topic, but the data used in previous studies do not include user feedbacks, or only the user's rating information for the movie is used in the feedback. Therefore, this study intends to apply community exploration and sentiment analysis to movie recommendations. At the same time, the user's feedbacks on the community platform are collected, the type of the movie he/she prefers is learnt through word processing and sentiment analysis. This study uses the results of sentiment analysis and the rating information to calculate other similar users and predict the rating of target user. After the experiment, the lowest MAE value is 0.7942. It shows that the predicted value is not far from the actual value and achieves better result.
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Lee, Chia-Hsing, and 李佳馨. "Integration of Content-based approach and Hybrid Collaborative Filtering for Movie Recommendation." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/64f874.
Full textAbstract:
碩士國立臺北科技大學
資訊與運籌管理研究所
101
As the scale of e-commerce continues to expand, personalized recommendation systems have been developed for general users in the hope of saving their search cost and time. In the core methods of personalized recommendation systems, collaborative filtering, one of the most widely-used recommended methods, still leaves two major problems. One is sparsity problem, the difficulty of finding similar users results in poor accuracy. The other is cold start, new users and new items make it hardly possible to estimate the preferences because of the lack of past ratings. This work simulates a real environment for movie recommendation. In the case of considering the factors of the new users and new movies in the sparse rating matrix, we conduct a content-based approach based on movie genre to predict user ratings on new movies. Furthermore, we integrate the modification of similar measures in memory-based collaborative filtering with matrix factorization(model-based collaborative filtering). In experiments, we observe our methodology brought out a lower MAE in overall rating prediction. Finally, our approach has been shown to have better recommendation quality than basic collaborative filtering in different sparsity level dataset.
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LIN, JI-LIN, and 林紀霖. "Applying Neural Network Model for User Rating Prediction in Movie Recommendation Systems." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/8b45n7.
Full textAbstract:
碩士輔仁大學
資訊工程學系碩士班
106
A recommendation system is information that can be useful to a user or that the user is interested in but he does not know. The amount of information on the Internet is too large. Users do not have time to read all the information, only the information provided by the hottest websites. Although there are powerful search engines (such as GOOGLE, Yahoo, Bing, etc.), but users don't necessarily know the keywords, so it's not easy to find relevant information The traditional recommendation system recommends the analysis by the user's viewed video scores, and analyzes the relevant data of each movie for classification (for example: the name of the type A movie, director, actors and keywords, etc.) Find out which user and other users who have seen the same movie and have similar ratings, and refer to the ratings of all the people who have seen the video to analyze the videos that the user has not seen. This recommendation method seems to work, because he has a great chance to like the movies that people with the same interests have. This is not necessarily true, because everyone likes something different, even if they like A-type movies, they don't mean they like all the A-type movies. And this method also requires a huge amount of viewing and analysis time to use, and each person's analysis results will not be exactly the same.
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Hsueh, Yi-Ching, and 薛怡靜. "The Design and Implementation of Movie Recommendation Platform Based on Text Feature." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/fr8w53.
Full textAbstract:
碩士國立中正大學
資訊工程研究所
107
In the era of Information explosion, a large amount of information has swarmed from all directions,and -people share every information on the Internet. In order to solve the problem of information overload and users searching, many platforms implement a recommendation system to keep customers staying on the platform. Recommendation system can also provide users useful and valuable content. Therefore, the recommendation system has become an important development trend of the network platform. The movie has become one of the human’s major leisure and entertainment event. In addition to the newly released movies, users usually find the movies they want to see according to the feature of relevance or the same category. Therefore, that the platform recommends interested movies to users, is an important task. In this thesis we implemented a movie recommendation platform. Users can search for movies, collect movies, view recent popular movies and view recommended movies through the platform. The platform will give recommendations based on the word vector similarity and feature weight of each movie, and will also give the user a recommended movie according to the user's favorite movie, favorite category or his/her browsing history in the platform.
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Chen, Wei-Yu, and 陳偉郁. "A Study on Context-aware Factors Affect Movie Recommendation Using AHP Approach." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/29194406460316897699.
Full textAbstract:
碩士國防大學
資訊管理學系
104
Nowadays a wide range of information is accumulated rapidly owing to booming Internet technology, inducing the problem of information overloaded. When users initiate search processes from the overloaded information often suffer from spending too much time to deal with. In order to solve this kind of problems, filtering and screening technology are proposed. User’s interest and needs of information can be satisfied efficiently by using the implemented “Recommendation Systems", saving search time and effort. In the aspect of movie recommendation, the traditional recommendation technologies only focus on "User" and "Recommended Items". Currently, the vast majority of the existing movie recommendation systems do not take into account of the context of information beyond the scope of “user” and “recommended items”, which cannot meet the needs of users. Therefore, by reviewing literatures and obtaining expertise from interviews with experts, this thesis concludes that the following four aspects of analysis facets and related contextual factors are important: "Movie Contextual Information", "Users’ Contextual Information", "Environmental Contextual Information" and "Social Contextual Information", respectively. We adopt AHP to evaluate the weights of the hierarchy between the various levels of analysis variables to each other, as the basis for assessing the impact of situational factors of movie recommendations. Through the study of interview with film-related workers and who used online movie recommendation systems, we find that the most important concern of movies recommended analytical facet is "Context of the Movie". The most important four contextual factors are "Story", "Personal Preference", "Social Relations" and "Actor", respectively. This thesis’s findings can provide contextual movie recommendation systems the references of selection of contextual factors for the implementation of recommendation algorithm.
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Altaf, Basmah. "A Study of Fairness and Information Heterogeneity in Recommendation Systems." Diss., 2019. http://hdl.handle.net/10754/660257.
Full textAbstract:
Recommender systems are an integral and successful application of machine learning
in e-commerce industry and in everyday lives of online users. Recommendation
algorithms are used extensively for news, musics, books, point of interests, or travel
recommendation as well as in many other domains. Although much focus has been
paid on improving recommendation quality, however, some real-world aspects are not
considered: How to ensure that top-n recommendations are fair and not biased due to any popularity boosting events, such as awards for movies or songs? How to recommend items to entities by explicitly considering information from heterogeneous
sources. What is the best way to model sequential recommendation systems as heterogeneous context-aware design, and learning on-the- y from spatial, temporal and social contexts. Can we model attributes and heterogeneous relations in a heterogeneous information network?
The goal of this thesis is to pave the way towards the next generation of realworld
recommendation systems tackling fairness and information heterogeneity challenges
to improve the user experience, while giving good recommendations. This thesis
bridges techniques from recommendation and deep-learning techniques for representation learning by proposing novel techniques to address the above real-world problems.
We focus on four directions: (1) model the e ect of popularity bias over time on the
consumption of items, (2) model the heterogeneous information associated with sequential history of users and social links for sequential recommendation, (3) model the heterogeneous links and rich content of nodes in an academic heterogeneous information network, and (4) learn semantics using topic modeling for nodes based on their content and heterogeneous links in a heterogeneous information network
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Jian, Ciao-Ting, and 簡巧婷. "Online Movie Recommendation Approach based on Collaborative Topic Modeling and Cross-Domain Analysis." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/atphhc.
Full textAbstract:
碩士國立交通大學
資訊管理研究所
105
With the rapid development of the Internet and the rise of new types of news websites with e-commerce portals, more and more users obtain specific topics online information. Successfully information recommendation to users by analyzing users’ browsing behaviors and preferences in the web-based platform can attract more users and enhance the information flow of platform, which is an important trend of the current online worlds. However, information provided by news websites is exploding and becoming more complicated. Therefore, it is an indispensable part of IT technology for e-commerce platforms to deploy appropriate online recommendation methods to improve the users’ click-through rates. In this research, we conduct cross-domain and diversity analysis of user preferences to develop novel online movie recommendation methods and evaluated online recommendation results. Specifically, association rule mining is conducted on user browsing news and moves to find the latent associations between news and movies. A novel online recommendation approach is proposed to predict user preferences for movies based on Latent Dirichlet Allocation, Collaborative Topic Modeling and the diversity of recommendations. The experimental results show that the proposed approach can improve the cold-start problem and enhance the click-through rate of movies.
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Huang, Wei Hao, and 黃威豪. "An Attribute-based Fuzzy Inference Approach for Movie Recommendation: A Model and Its Evaluation." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/44517240468695339990.
Full textAbstract:
碩士輔仁大學
資訊管理學系
99
Recommendation techniques are widely applied in electronic commerce to predict users’ preferences based on the user’s search behavior on the Web sites and recommend items that may be interest. Collaborative filtering techniques are also widely used by e-commerce sites to provide recommendations to customers based on the preferences of similar users. However, as the numbers of customers and products increase, the prediction accuracy of collaborative filtering algorithms declines because of sparse ratings. In addition, the time complexity for collaborative filtering is high during the training and predication phase. This research proposed the attribute-based fuzzy inference and cluster (AFIC) approach for movie recommendation. Furthermore, the modified content-based, collaborative filtering are our baseline approaches in comparison with the performance of the proposed AFIC approach. A series of experiments have been conducted to show that the proposed AFIC approach can achieve highest precision in comparison with the tradition approaches. Moreover, the computational time can be decreased with the aid of the α-cuts approach. The results have implications for design the interactive movie recommendation system based on the proposed approach.
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Hsieh, Chin-Yu, and 謝金育. "A Recommendation Mechanism Combined with Bayesian Networks and Incentive Theory-A Movie Recommend System Design." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/u3yn87.
Full textAbstract:
碩士國立交通大學
管理學院資訊管理學程
101
The recommendation systems are widely used on the network to help users quickly find suitable or interested products. In this area, many recommendation techniques, such as Content-based Approach, Collaborative Filtering Approach, and Hybrid Approach, have been developed. Although the recommendation technology has become mature, there are still some problems. Recommendation systems cannot provide the correct information if we don’t have enough user information. The precision of the recommendation system will be increased dramatically, if a system gets more user potential information. In this study, we attempts to propose a recommendation mechanism design combined with Bayesian networks and incentive theory. In lack of user profile information, our recommendation mechanism still has high precision. This mechanism approach uses Bayesian network to generate association rules from user profiles, a source of information used to expand the user profile, and avoid the problem of user profile shortage. In the new items problem, based on the theory of incentives, we propose a mechanism for encouraging data sharing. This encouraging mechanism satisfies individual rationality and incentive compatibility. Our experiments show that our proposed mechanism can significantly improve the performance of a recommender system under the short user profile situation.
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Altaf, Basmah. "Modeling Temporal Bias of Uplift Events in Recommender Systems." Thesis, 2013. http://hdl.handle.net/10754/292299.
Full textAbstract:
Today, commercial industry spends huge amount of resources in advertisement campaigns, new marketing strategies, and promotional deals to introduce their product to public and attract a large number of customers. These massive investments by a company are worthwhile because marketing tactics greatly influence the consumer behavior. Alternatively, these advertising campaigns have a discernible impact on recommendation systems which tend to promote popular items by ranking them at the top, resulting in biased and unfair decision making and loss of customers’ trust. The biasing impact of popularity of items on recommendations, however, is not fixed, and varies with time. Therefore, it is important to build a bias-aware recommendation system that can rank or predict items based on their true merit at given time frame.
This thesis proposes a framework that can model the temporal bias of individual items defined by their characteristic contents, and provides a simple process for bias correction. Bias correction is done either by cleaning the bias from historical training data that is used for building predictive model, or by ignoring the estimated bias from the predictions of a standard predictor. Evaluated on two real world datasets, NetFlix and MovieLens, our framework is shown to be able to estimate and remove
the bias as a result of adopted marketing techniques from the predicted popularity of
items at a given time.
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Zhang, Chuan-Heng, and 張傳珩. "Text Mining and Sentiment Analysis for the Application of the Product Recommendation-The Case of PTT Movie Board." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/62w2x2.
Full textAbstract:
碩士東吳大學
資訊管理學系
107
Thanks to internet technology improvements and the smart devices popularized, we can find a huge variety of information and different kind of social media platforms. Nowadays people prefer to search for comments and information on the internet than ask others opinions before they make purchases. However, there is massive information around the internet world. When people use the keywords to search in the comments, they will have to read a lot of texts and pages, which will take a bunch of time. This is not an easy job for people. The research "Subject analysis" and "Emotional analysis" help people to search for the diversity of emotional analysis consequences from movies. People won't have to review many comments to understand the movie evaluation. By collecting the half-year comments from PTT, this research has analyzed the adjective words to get the emotional score and use the score to build movie recommendations. After that, analyze the topics to get the topic models including the emotional score from analyzed words to give people the movie they prefer.
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Chun-Hua, Tai, and 戴君樺. "Interactive Movie Recommendations with Strengthened Analysis of Genre Preferences." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/31465866774658426149.
Full textAbstract:
碩士輔仁大學
資訊管理學系碩士班
103
E-commerce often relies upon recommendation techniques given their potential commercial value, as well as recommendation systems’ capacity for accurate prediction that can boost websites’ conversion rates, promote the electronic trade of goods, and increase company sales. For these systems, collaborative filtering (CF) enables websites to recommend products for target users based on the preferences of peers with similar interests. While CF can expand a user’s profile of interests, it cannot overcome the problems of cold start and sparse ratings (i.e., an individual can vote only for a small fraction of all items). Since recent studies have shown that the stability of users’ preferences influences their decision making, especially concerning experiential goods (e.g., movies, books), measuring such stability regarding these goods is worth investigating. This study thus proposes integrating genre- and director-based anchoring processes to identify users’ preferences for movie genres and measure preference stability in order to provide more precisely personalized recommendations. Specifically, we overcome the problem of sparse ratings by analyzing associations among movie genres as well as the correlations of the director and genre as means to pinpoint genre-based associations. By employing the analytic hierarchy process (AHP), we thus infer user preferences for movie genres via a series of interactive genre- and director-based anchoring processes, which ultimately provides effective, precisely interactive movie recommendations.
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Tu, Ming-Chieh, and 杜明潔. "Consumer recommendation of movies based on eWOM." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/78835877207007741197.
Full textAbstract:
碩士中原大學
資訊管理研究所
99
For users of recommendation system, obtaining recommendation list with new products are more useful than old products. The recommendation based on electronic word-of-mouth (eWOM) usually offers old products to consumer, because the recommendation has no enough eWOM in initial. This study is devoted to use eWOM of old products to predict rating of new products to consumer. Different from traditional approaches that treat reviews of one consuming event as a whole; our approach considers the relation between these consuming events. The mechanism is using eWOM to evaluate each attribute of old product, and takes ontological techniques to construct the relation between old products and new products of the same kind by the attributes. By matching final good rating of new product, experiment showed that the accuracy of our system was 73.33%. The result was lower than 80% accuracy of the collaborative predicting, but we could conclude that the proposed scheme compared with collaborative predicting should help decrease the cost of human resource and time.
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Niu, Yun-Fang, and 鈕韻芳. "Integrating the anchor theory with preference stability for interactive movie recommendations." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/71122055803732856586.
Full textAbstract:
碩士輔仁大學
資訊管理學系
100
Recommendation techniques are utilized in electronic commerce because of their potential commercial values. Many e-commerce sites employ collaborative filtering techniques to provide recommendations to customers based on the preferences of similar users. However, as the numbers of customers and products increase, the prediction accuracy of collaborative filtering algorithms declines because of sparse ratings and cold-start. In addition, the traditional recommended approaches just consider the item’s attribute and the preference similarities between users; however, they are not concerned that users’ preferences may be developed with growth in their familiarity with or experience during choice or preference elicitation. In this work, we propose an interactive anchoring process, i.e., a pair-wise preference comparison process with the G-Fuzzy hybrid filtering approach to capture the user’s preferences of movie genres more naturally and then achieve effective interactive recommendations. Then, we also discuss the influence of different types of user’s preference for developing the recommendation strategies. The experiment results show that the proposed Anchor-based hybrid filtering approach can achieve effective recommendations especially for the user who has the unstable preference of movie genres. Moreover, the Anchor-based hybrid filtering approach can effectively capture the user’s preference naturally and filter out the user’s disliked movies.
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Kelly, Erin Joy. "Marketing health issues to tweens : recommendations for reaching this demographic more effectively." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-12-4446.
Full textAbstract:
This paper explores public health campaigns as they relate to tweens and their use of technology. After considering how this demographic utilizes both traditional and new media, further examination was done on general health problems that affect this group. Three major health issues were then chosen for analysis. A relevant campaign for each issue was also evaluated, as were its overall marketing and communication efforts. The health problems and corresponding campaigns chosen include childhood obesity and the “Let’s Move” campaign, electronic aggression and the “Stop Bullying” campaign and youth suicide and the “WeCanHelpUs” campaign. From these analyses, recommendations for ways to improve each campaign were provided, as were general conclusions for reaching this demographic more efficiently and effectively.text