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Journal articles on the topic 'Movie recommendation'

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

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1

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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M, Shobana. "Movie Recommendation System using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4925–29. http://dx.doi.org/10.22214/ijraset.2021.35990.

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A movie recommendation is important in our social life due to its strength in providing enhanced entertainment. Such a system can suggest a set of movies to users based on their interest, or the popularities of the movies. A recommendation system is used for the purpose of suggesting items to purchase or to see. They direct users towards those items which can meet their needs through cutting down large database of Information. A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. They are primarily used in commercial applications. MOVREC also help users to find the movies of their choices based on the movie experience of other users in efficient and effective manner without wasting much time in useless browsing
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Manjeet Singh and Namita Goyal. "Collaborative Filtering Movie Recommendation System." International Journal for Modern Trends in Science and Technology 6, no. 12 (January 1, 2021): 471–73. http://dx.doi.org/10.46501/ijmtst061291.

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Recommendation System plays an important role in today’s era of e-commerce. From OTT platforms to the shopping application and music application everywhere we see that after watching a movie or buying an item, listening a song we are recommended with some other movie or item or song. Most of the time we select our next movie, item or song from the recommended one. In this paper I will give you a brief description of collaborative and user-based filtering. The data used in this research is taken from Movie Lens. The result obtained contains some movie recommendations.
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ÇATALTEPE, Zehra, Mahiye ULUYAĞMUR, and Esengül TAYFUR. "Feature selection for movie recommendation." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 24 (2016): 833–48. http://dx.doi.org/10.3906/elk-1303-189.

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Deshmukh, Puja, and Geetanjali Kale. "Music and Movie Recommendation System." International Journal of Engineering Trends and Technology 61, no. 3 (July 25, 2018): 178–81. http://dx.doi.org/10.14445/22315381/ijett-v61p229.

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Liu, Jingdong, Won-Ho Choi, and Jun Liu. "Personalized Movie Recommendation Method Based on Deep Learning." Mathematical Problems in Engineering 2021 (February 19, 2021): 1–12. http://dx.doi.org/10.1155/2021/6694237.

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With the rapid development of network technology and entertainment creation, the types of movies have become more and more diverse, which makes users wonder how to choose the type of movies. In order to improve the selection efficiency, recommend Algorithm came into being. Deep learning is a research field that has received extensive attention from scholars in recent years. Due to the characteristics of its deep architecture, deep learning models can learn more complex structures. Therefore, deep learning algorithms in speech recognition, machine translation, image recognition, and other fields have achieved impressive results. This article mainly introduces the research of personalized movie recommendation methods based on deep learning and intends to provide ideas and directions for the research of personalized movie recommendation under deep learning. This paper proposes a research method of personalized movie recommendation methods based on deep learning, including an overview of personalized recommendation and collaborative filtering recommendation algorithms, which are used to conduct research experiments on personalized movie recommendation methods based on deep learning. The experimental results in this paper show that the accuracy of the training set of the Seq2Seq model based on the LSTM recurrent neural network reaches 96.27% and the accuracy of the test set reaches 95.89%, which can be better for personalized movie recommendation.
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Aditya, T. S., Karthik Rajaraman, and M. Monica Subashini. "Comparative Analysis of Clustering Techniques for Movie Recommendation." MATEC Web of Conferences 225 (2018): 02004. http://dx.doi.org/10.1051/matecconf/201822502004.

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Movie recommendation is a subject with immense ambiguity. A person might like a movie but not a very similar movie. The present recommending systems focus more on just few parameters such as Director, cast and genre. A lot of Power intensive methods such as Deep Convolutional Neural Network (CNN) has been used which demands the use of Graphics processors that require more energy. We try to accomplish the same task using lesser Energy consuming algorithms such as clustering techniques. In this paper, we try to create a more generalized list of similar movies in order to provide the user with more variety of movies which he/she might like, using clustering algorithms. We will compare how choosing different parameters and number of features affect the cluster's content. Also, compare how different algorithms such as K-mean, Hierarchical, Birch and mean shift clustering algorithms give a varied result and conclude which method will suit for which scenarios of movie recommendations. We also conclude on which algorithm clusters stray data points more efficiently and how different algorithms provide different advantages and disadvantages.
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Ng, Yiu-Kai. "MovRec: a personalized movie recommendation system for children based on online movie features." International Journal of Web Information Systems 13, no. 4 (November 6, 2017): 445–70. http://dx.doi.org/10.1108/ijwis-05-2017-0043.

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Purpose The purpose of this study is to suggest suitable movies for children among the various multimedia selections available these days. Multimedia have a significant impact on the social and psychological development of children who are often explored to inappropriate materials, including movies that are either accessible online or through other multimedia channels. Even though not all movies are bad, there are negative effects of offensive languages, violence and sexuality as exhibited in movies. Parents and guidance of children need all the help they can get to promote the healthy use of movies these days. Design/methodology/approach To offer parents appropriate movies of interest to their youths, the authors have developed MovRec, a personalized movie recommender for children, which is designed to provide educational and suitable entertaining opportunities for children. MovRec determines the appealingness of a movie for a particular user based on its children-appropriate score computed by using the backpropagation model, pre-defined category using latent Dirichlet allocation, its predicted rating using matrix factorization and sentiments based on its users’ reviews, which along with its like/dislike count and genres, yield the features considered by MovRec. MovRec combines these features by using the CombMNZ model to rank and recommend movies. Findings The performance evaluation of MovRec clearly demonstrates its effectiveness and its recommended movies are highly regarded by its users. Originality/value Unlike Amazon and other online movie recommendation systems, such as Common Sense Media, Internet Movie Database and TasteKid, MovRec is unique, as to the best of the authors’ knowledge, MovRec is the first personalized children movie recommender.
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Wang, Yibo, Mingming Wang, and Wei Xu. "A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework." Wireless Communications and Mobile Computing 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/8263704.

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Movie recommendation in mobile environment is critically important for mobile users. It carries out comprehensive aggregation of user’s preferences, reviews, and emotions to help them find suitable movies conveniently. However, it requires both accuracy and timeliness. In this paper, a movie recommendation framework based on a hybrid recommendation model and sentiment analysis on Spark platform is proposed to improve the accuracy and timeliness of mobile movie recommender system. In the proposed approach, we first use a hybrid recommendation method to generate a preliminary recommendation list. Then sentiment analysis is employed to optimize the list. Finally, the hybrid recommender system with sentiment analysis is implemented on Spark platform. The hybrid recommendation model with sentiment analysis outperforms the traditional models in terms of various evaluation criteria. Our proposed method makes it convenient and fast for users to obtain useful movie suggestions.
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Li, Jing, Wentao Xu, Wenbo Wan, and Jiande Sun. "Movie recommendation based on bridging movie feature and user interest." Journal of Computational Science 26 (May 2018): 128–34. http://dx.doi.org/10.1016/j.jocs.2018.03.009.

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Deldjoo, Yashar, Maurizio Ferrari Dacrema, Mihai Gabriel Constantin, Hamid Eghbal-zadeh, Stefano Cereda, Markus Schedl, Bogdan Ionescu, and Paolo Cremonesi. "Movie genome: alleviating new item cold start in movie recommendation." User Modeling and User-Adapted Interaction 29, no. 2 (February 26, 2019): 291–343. http://dx.doi.org/10.1007/s11257-019-09221-y.

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Desai, Harshali. "Movie Recommendation System through Movie Poster using Deep Learning Technique." International Journal for Research in Applied Science and Engineering Technology 9, no. 4 (April 30, 2021): 1574–81. http://dx.doi.org/10.22214/ijraset.2021.33947.

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Malve, Gandhali, Lajree Lohar, Tanay Malviya, and Shirish Sabnis. "Movie Recommenadation System." International Journal on Recent and Innovation Trends in Computing and Communication 9, no. 4 (April 30, 2021): 13–16. http://dx.doi.org/10.17762/ijritcc.v9i4.5460.

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Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item.
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Vlachos, Michail, and Daniel Svonava. "Recommendation and visualization of similar movies using minimum spanning dendrograms." Information Visualization 12, no. 1 (April 30, 2012): 85–101. http://dx.doi.org/10.1177/1473871612439644.

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Exploration of graph structures is an important topic in data mining and data visualization. This work presents a novel technique for visualizing neighbourhood and cluster relationships in graphs; we also show how this methodology can be used within the setting of a recommendation system. Our technique works by projecting the original object distances onto two dimensions while carefully retaining the ‘backbone’ of important distances. Cluster information is also overlayed on the same projected space. A significant advantage of our approach is that it can accommodate both metric and non-metric distance functions. Our methodology is applied to a visual recommender system for movies to allow easy exploration of the actor–movie bipartite graph. The work offers intuitive movie recommendations based on a selected pivot movie and allows the interactive discovery of related movies based on both textual and semantic features.
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KITAMURA, Yutaro, Kazuhito SAWASE, and Hajime NOBUHARA. "Formal Concept Analysis Based Movie Recommendation." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 25, no. 1 (2013): 624–35. http://dx.doi.org/10.3156/jsoft.25.624.

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You, Sang-Hyun, Jeawon Park, and Jaehyun Choi. "Personal Preference Based Movie Recommendation System." International Journal of Multimedia and Ubiquitous Engineering 9, no. 9 (September 30, 2016): 11–18. http://dx.doi.org/10.14257/ijmue.2016.11.9.02.

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Hemantha, G., and Seyedmahmoud Talebi. "Movie Recommendation based on Users' Tweets." International Journal of Computer Applications 141, no. 14 (May 17, 2016): 34–36. http://dx.doi.org/10.5120/ijca2016909992.

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Hwang, Tae-Gyu, and Sung Kwon Kim. "Movie Recommendation Algorithm using Genre Correlation." KIISE Transactions on Computing Practices 26, no. 9 (September 30, 2020): 429–34. http://dx.doi.org/10.5626/ktcp.2020.26.9.429.

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Lekakos, George, and Petros Caravelas. "A hybrid approach for movie recommendation." Multimedia Tools and Applications 36, no. 1-2 (December 21, 2006): 55–70. http://dx.doi.org/10.1007/s11042-006-0082-7.

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Hwang, Tae-Gyu, and Sung Kwon Kim. "Movie Recommendation through Multiple Bias Analysis." Applied Sciences 11, no. 6 (March 22, 2021): 2817. http://dx.doi.org/10.3390/app11062817.

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A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.
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Vibhandik, Ghanashyam. "Movie Recommendation System using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4778–81. http://dx.doi.org/10.22214/ijraset.2021.35741.

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Movies are very significant in our lives. It is one of the many forms of entertainment that we encounter in our daily lives. It is up to the individual to decide whatever type of film they choose to see, whether it is a comedy, romantic film, action film, or adventure film. However, the issue is locating acceptable content, as there is a large amount of information created each year. As a result, finding our favourite film is really difficult. The goal of this research is to improve the regular filtering technique's performance and accuracy. A recommendation system can be implemented using a variety of approaches. Content-based filtering and collaborative filtering strategies are employed in this work. The content-based filtering approach analyses the user's history/past behaviour and recommends a list of comparable movies depending on their input. K-NN algorithms and collaborative filtering are also employed in this paper to improve the accuracy of the results. Cosine similarity is utilised in this work to quickly discover comparable information. The correctness of the cosine angle is measured by cosine similarity. People may quickly find their favourite movie content thanks to all of this.
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Ibrahim, Muhammad, Imran Sarwar Bajwa, Riaz Ul-Amin, and Bakhtiar Kasi. "A Neural Network-Inspired Approach for Improved and True Movie Recommendations." Computational Intelligence and Neuroscience 2019 (August 4, 2019): 1–19. http://dx.doi.org/10.1155/2019/4589060.

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In the last decade, sentiment analysis, opinion mining, and subjectivity of microblogs in social media have attracted a great deal of attention of researchers. Movie recommendation systems are the tools, which provide valuable services to the users. The data available online are growing gradually because the online activities of users or viewers are increasing day by day. Because of this, big data, analytics, and computational issues have raised. Therefore, we have to improve recommendations services upon the traditional one to make the recommendation system significant and efficient. This article presents the solution for these issues by producing the significant and efficient recommendation services using multivariates (ratings, votes, Twitter likes, and reviews) of movies from multiple external resources which are fetched by the web bot and managed by the Apache Hadoop framework in a distributed manner. Reviews are analyzed by a deep semantic analyzer based on the recurrent neural network (RNN/LSTM attention) with user movie attention (UMA) to produce the emotion. The proposed recommender evaluates multivariates and produces a more significant movie recommendation list according to the taste of the user on a mobile app in an efficient way.
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Zhubatkhan, A. Y., Z. A. Buribayev, S. S. Aubakirov, M. D. Dilmagambetova, and S. A. Ryskulbek. "COMPARISON MODELS OF MACHINE LEARNING FOR MOVIE RECOMMENDATION SYSTEMS." PHYSICO-MATHEMATICAL SERIES 335, no. 1 (February 8, 2021): 26–31. http://dx.doi.org/10.32014/2021.2224-5294.4.

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The trend of the Internet makes the presentation of the right content for the right user inevitable. To this end, recommendation systems are used in areas such as music, books, movies, travel planning, e-commerce, education, and more. One of the most popular recommendation systems in the world is Netflix, which generated record profits during quarantine in the first quartile of 2020. The systematic approach of recommendations is based on the history of user selections, likes and reviews, each of which is interpreted to predict future user selections. This article provides a meaningful analysis of various recommendation systems, such as content-based, collaborative filtering and popularity. We reviewed 7 articles published from 2005 to 2019 to discuss issues related to existing models. The purpose of this article is to compare machine learning algorithms in the Surprise library for a recommendation system. Recommendation system has been implemented and quality has been evaluated using the MAE and RMSE metrics.
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Zhubatkhan, A. Y., Z. A. Buribayev, S. S. Aubakirov, M. D. Dilmagambetova, and S. A. Ryskulbek. "COMPARISON MODELS OF MACHINE LEARNING FOR MOVIE RECOMMENDATION SYSTEMS." PHYSICO-MATHEMATICAL SERIES 335, no. 1 (February 8, 2021): 26–31. http://dx.doi.org/10.32014/2021.2518-1726.4.

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The trend of the Internet makes the presentation of the right content for the right user inevitable. To this end, recommendation systems are used in areas such as music, books, movies, travel planning, e-commerce, education, and more. One of the most popular recommendation systems in the world is Netflix, which generated record profits during quarantine in the first quartile of 2020. The systematic approach of recommendations is based on the history of user selections, likes and reviews, each of which is interpreted to predict future user selections. This article provides a meaningful analysis of various recommendation systems, such as content-based, collaborative filtering and popularity. We reviewed 7 articles published from 2005 to 2019 to discuss issues related to existing models. The purpose of this article is to compare machine learning algorithms in the Surprise library for a recommendation system. Recommendation system has been implemented and quality has been evaluated using the MAE and RMSE metrics.
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Rismala, Rita, Rudy Prabowo, and Agung Toto Wibowo. "Pairwise Preference Regression on Movie Recommendation System." Indonesian Journal on Computing (Indo-JC) 4, no. 1 (March 22, 2019): 57. http://dx.doi.org/10.21108/indojc.2019.4.1.255.

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Recommendation System is able to help users to choose items, including movies, that match their interests. One of the problems faced by recommendation system is cold-start problem. Cold start problem can be categorized into three types, they are: recommending existed item for new user, recommending new item for existed user, and recommending new item for new user. Pairwise preference regression is a method that directly deals with cold-start problem. This method can suggest a recommendation, not only for users who have no historical rating, but also for those who only have less demographic info. From the experiment result, the best score of Normalized Discounted Cumulative Gain (nDGC) from the system is 0.8484. The standard deviation of rating resulted by the recommendation system is 1.24, the average is 3.82. Consequently, the distribution of recommendation result is around rating 5 to 3. Those results mean that this recommendation system is good to solving cold-start problem in movie recommendation system.
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B, Sreelekshmi, and Vidya N. "Survey on Kernel Optimization based Enhanced Preference Learning for Online Movie Recommendation." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1835–42. http://dx.doi.org/10.31142/ijtsrd11305.

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36

Nguyen, Sang Thi Thanh, and Bao Duy Tran. "Long Short-Term Memory Based Movie Recommendation." Science & Technology Development Journal - Engineering and Technology 3, SI1 (September 19, 2020): SI1—SI9. http://dx.doi.org/10.32508/stdjet.v3isi1.540.

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Recommender systems (RS) have become a fundamental tool for helping users make decisions around millions of different choices nowadays – the era of Big Data. It brings a huge benefit for many business models around the world due to their effectiveness on the target customers. A lot of recommendation models and techniques have been proposed and many accomplished incredible outcomes. Collaborative filtering and content-based filtering methods are common, but these both have some disadvantages. A critical one is that they only focus on a user's long-term static preference while ignoring his or her short-term transactional patterns, which results in missing the user's preference shift through the time. In this case, the user's intent at a certain time point may be easily submerged by his or her historical decision behaviors, which leads to unreliable recommendations. To deal with this issue, a session of user interactions with the items can be considered as a solution. In this study, Long Short-Term Memory (LSTM) networks will be analyzed to be applied to user sessions in a recommender system. The MovieLens dataset is considered as a case study of movie recommender systems. This dataset is preprocessed to extract user-movie sessions for user behavior discovery and making movie recommendations to users. Several experiments have been carried out to evaluate the LSTM-based movie recommender system. In the experiments, the LSTM networks are compared with a similar deep learning method, which is Recurrent Neural Networks (RNN), and a baseline machine learning method, which is the collaborative filtering using item-based nearest neighbors (item-KNN). It has been found that the LSTM networks are able to be improved by optimizing their hyperparameters and outperform the other methods when predicting the next movies interested by users.
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Hayashi, Takahiro, and Rikio Onai. "Movie Recommendation Using Reviews on the Web." Transactions of the Japanese Society for Artificial Intelligence 30, no. 1 (2015): 102–11. http://dx.doi.org/10.1527/tjsai.30.102.

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Soni, Karan, Rinky Goyal, Bhagyashree Vadera, and Siddhi More. "A Three Way Hybrid Movie Recommendation Syste." International Journal of Computer Applications 160, no. 9 (February 15, 2017): 29–32. http://dx.doi.org/10.5120/ijca2017913026.

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Jain, Arpita, and Santosh K. "Collaborative Filtering for Movie Recommendation using RapidMiner." International Journal of Computer Applications 169, no. 6 (July 17, 2017): 29–33. http://dx.doi.org/10.5120/ijca2017914771.

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Arriaga, Patrícia, Joana Alexandre, Octavian Postolache, Manuel J. Fonseca, Thibault Langlois, and Teresa Chambel. "Why Do We Watch? The Role of Emotion Gratifications and Individual Differences in Predicting Rewatchability and Movie Recommendation." Behavioral Sciences 10, no. 1 (December 19, 2019): 8. http://dx.doi.org/10.3390/bs10010008.

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Background: The present study’s main aim was to determine the predictors of movie rewatchability and recommendations. Methods: Using a sample of 318 participants, we first tested the structure of a gratification scale from watching a movie. Then, we examined the role of age, need for cognition, need for affect, extraversion, and emotional gratifications, in predicting individuals’ interest in rewatching the movie and in making recommendations. Results: As in the original proposal of the emotional gratification scale, the following dimensions were identified: fun, thrill, empathic sadness, release of emotions, social sharing, contemplative experiences, and character engagement, with acceptable model fit and reliability, convergent and divergent validity. Social sharing, contemplate experiences, need for affect and age were significant predictors of movie recommendation; whereas social sharing, thrill, extraversion, and age contributed most to explaining rewatching interest. Conclusion: This study highlights the importance of considering distinct gratifications and individual differences in predicting rewatching and movie recommendation.
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Parthasarathy, Jayaraman, and Ramesh Babu Kalivaradhan. "Collaborative Filtering-Based Recommendation System Using Time Decay Model." International Journal of e-Collaboration 17, no. 3 (July 2021): 85–100. http://dx.doi.org/10.4018/ijec.2021070106.

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Online collaborative movie recommendation systems attempt to help customers accessing their favourable movies by gathering exactly comparable neighbors between the movies from their chronological identical ratings. Collaborative filtering-based movie recommendation systems require viewer-specific data, and the need for collecting viewer-specific data diminishes the effectiveness of the recommendation. To solve this problem, the authors employ an effective multi-armed bandit called upper confidence bound, which is applied to automatically recommend the movies for the users. In addition, the concept of time decay is provided in a mathematical definition that redefines the dynamic item-to-item similarity. Then, two patterns of time decay are analyzed, namely concave and convex functions, for simulation. The experiment test the MovieLens 100K dataset. The proposed method attains a maximum F-measure of 98.45 whereas the existing method reaches a minimum F-measure of only 95.60. The presented model adaptively responds to new users, can provide a better service, and generate more user engagement.
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Dhiwar S., Ms Pooja. "Movie Review System using Sentiment Analysis and Social Networking Platforms (SNPs)." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1492–99. http://dx.doi.org/10.22214/ijraset.2021.35308.

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Today online social networking platforms SNPs have become an integral part of or life where we share a lot of information of all the things, we do in life from shopping to movie watching. With ever growing use of SNPs recommendation systems have emerged as a hot trend for applications in e-commerce and digital media. These recommendation systems are useful as well as misguiding. Today digital media use has increased tremendously with increase in internet speeds. But users do not get proper review of a movie and a user is lured to watch a substandard movie which he does not intended to do, thus costing a user time and money. So, there is a need of developing a movie review which will give correct reviews of a digital content like movies so he can only movies which he intends to do. So, we are studying various techniques authored by various authors and create a good movie review system of our own. The first technique we studied and intends to use is movie recommendation system using tweets. The second study is movie recommendation using similarity measures. The third study does find a public shamming using SNP. These techniques are useful and we propose to use some part of each in our new movie review framework by improving the techniques drawbacks. The new framework will be a combination of data from more than one SNP and using natural language processing and machine learning on the data. We are going to use two machine learning algorithms SVM and Naïve Bayes for this purpose. For natural language processing of SNP data, we are going to use OPEN-NLP. We intend to use SNPs such as Twitter and any other movie database like IMDB etc. for data on the movie. The movie will be classified in three classes bad, good and excellent. The results from each algorithm SVM and Naïve bayes will be analyzed for each SNP and try to give user a more accurate movie review by combining all the reviews together and classes accuracy and show overall prediction results with a rating. To get more accurate results for each movie we are going to create a dataset for each movie for demonstration and will not depend on a single combined dataset as keywords for each movie may be different. We are going to combine datasets for each movie from multiple SNPs.
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Li, Min, Yingming Zeng, Yue Guo, and Yun Guo. "A Movie Recommendation System Based on Differential Privacy Protection." Security and Communication Networks 2020 (December 16, 2020): 1–10. http://dx.doi.org/10.1155/2020/6611463.

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In the past decades, the ever-increasing popularity of the Internet has led to an explosive growth of information, which has consequently led to the emergence of recommendation systems. A series of cloud-based encryption measures have been adopted in the current recommendation systems to protect users’ privacy. However, there are still many other privacy attacks on the local devices. Therefore, this paper studies the encryption interference of applying a differential privacy protection scheme on the data in the user’s local devices under the assumption of an untrusted server. A dynamic privacy budget allocation method is proposed based on a localized differential privacy protection scheme while taking the specific application scene of movie recommendation into consideration. What is more, an improved user-based collaborative filtering algorithm, which adopts a matrix-based similarity calculation method instead of the traditional vector-based method when computing the user similarity, is proposed. Finally, it was proved by experimental results that the differential privacy-based movie recommendation system (DP-MRE) proposed in this paper could not only protect the privacy of users but also ensure the accuracy of recommendations.
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Jain, Arushi, and Vishal Bhatnagar. "Movie Analytics for Effective Recommendation System using Pig with Hadoop." International Journal of Rough Sets and Data Analysis 3, no. 2 (April 2016): 82–100. http://dx.doi.org/10.4018/ijrsda.2016040106.

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Movies have been a great source of entertainment for the people ever since their inception in the late 18th century. The term movie is very broad and its definition contains language and genres such as drama, comedy, science fiction and action. The data about movies over the years is very vast and to analyze it, there is a need to break away from the traditional analytics techniques and adopt big data analytics. In this paper the authors have taken the data set on movies and analyzed it against various queries to uncover real nuggets from the dataset for effective recommendation system and ratings for the upcoming movies.
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Sharma, Mugdha, Laxmi Ahuja, and Vinay Kumar. "A Novel Rule based Data Mining Approach towards Movie Recommender System." Journal of information and organizational sciences 44, no. 1 (June 25, 2020): 157–70. http://dx.doi.org/10.31341/jios.44.1.7.

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The proposed research work is an effort to provide accurate movie recommendations to a group of users with the help of a rule-based content-based group recommender system. The whole approach is categorized into 2 phases. In phase 1, a rule- based approach has been proposed which considers the users’ viewing history to provide the Rule Base for every individual user. In phase 2, a novel group recommendation system has been proposed which considers the ratings of the movies as per the rule base generated in phase 1. Phase 2 also considers the weightage of every individual member of the group to provide the accurate movie recommendation to that particular group of users. The results of experimental setup also establish the fact that the proposed system provides more accurate outcomes in terms of precision and recall over other rule learning algorithms such as C4.5.
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Kim, Jaeyoung, and Seok-Won Lee. "The Ontology Based, the Movie Contents Recommendation Scheme, Using Relations of Movie Metadata." Journal of Intelligence and Information Systems 19, no. 3 (September 30, 2013): 25–44. http://dx.doi.org/10.13088/jiis.2013.19.3.025.

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47

Baazaoui, Hajer, and Mohamed Ramzi Haddad. "A Framework for Users’ Attitude Prediction Based on Visual Features: Application to Movies Recommendations." Journal of Information & Knowledge Management 17, no. 04 (December 2018): 1850037. http://dx.doi.org/10.1142/s0219649218500375.

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Recommendation and personalisation approaches aim to filter the most interesting resources that may attract users’ personal interests and preferences by analysing their past attitudes and consumption patterns. The visual aspect of content constitutes an important factor that drives consumers’ attitudes and decisions. In this context, this work proposes a framework for users’ attitude prediction based on items’ visual descriptors and details one of its possible applications for movies recommendations. The main idea of our proposal is to model users’ interests and consumption behaviors using the movies’ posters images and extract features based on the visual descriptors of the items that they interact with, in order to better predict their attitudes towards the ones they do not know. The recommendation approach was integrated into a movies recommendation application that visually assists users while searching for relevant movies to watch, by finding similar movie posters based on the visual aspects of the poster image.
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Awan, Mazhar Javed, Rafia Asad Khan, Haitham Nobanee, Awais Yasin, Syed Muhammad Anwar, Usman Naseem, and Vishwa Pratap Singh. "A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach." Electronics 10, no. 10 (May 20, 2021): 1215. http://dx.doi.org/10.3390/electronics10101215.

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In this era of big data, the amount of video content has dramatically increased with an exponential broadening of video streaming services. Hence, it has become very strenuous for end-users to search for their desired videos. Therefore, to attain an accurate and robust clustering of information, a hybrid algorithm was used to introduce a recommender engine with collaborative filtering using Apache Spark and machine learning (ML) libraries. In this study, we implemented a movie recommendation system based on a collaborative filtering approach using the alternating least squared (ALS) model to predict the best-rated movies. Our proposed system uses the last search data of a user regarding movie category and references this to instruct the recommender engine, thereby making a list of predictions for top ratings. The proposed study used a model-based approach of matrix factorization, the ALS algorithm along with a collaborative filtering technique, which solved the cold start, sparse, and scalability problems. In particular, we performed experimental analysis and successfully obtained minimum root mean squared errors (oRMSEs) of 0.8959 to 0.97613, approximately. Moreover, our proposed movie recommendation system showed an accuracy of 97% and predicted the top 1000 ratings for movies.
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Raigoza, Jaime, and Vikrantsinh Karande. "A Study and Implementation of a Movie Recommendation System in a Cloud-based Environment." International Journal of Grid and High Performance Computing 9, no. 1 (January 2017): 25–36. http://dx.doi.org/10.4018/ijghpc.2017010103.

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The availability of huge amounts of data in recent years have led users to being faced with an overload of choices. The outcome is a growth on the importance of recommendation systems due to their ability to solve this choice overload problem, by providing users with the most relevant products from many possible choices. For producing recommendations, things like a user's psychological profile, their browsing history and movie ratings from other users can be considered. To determine how strongly two user's behavior are related to each other, a Pearson correlation coefficient value is often calculated. In this paper, we study the recommendation system on a proposed cloud based environment to produce a list of recommended movies based on a user's profile information. Based on the Software-as-a-Service (SaaS) model implemented, we discuss the concepts such as collaborative filtering and content-based filtering. Given a MovieLens data-set, our results indicate that the proposed approach can provide a high performance in terms of precision, and generate more reliable and personalized movie recommendations, when given a greater number of movies rated by a user. An evaluation was done under minimal known data, which commonly leads to the cold-start problem.
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Raghav Mehta and Shikha Gupta. "Movie Recommendation Systems using Sentiment Analysis and Cosine Similarity." January 2021 7, no. 01 (January 4, 2021): 16–22. http://dx.doi.org/10.46501/ijmtst0701004.

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As Artificial Intelligence and Machine Learning is growing at a rapid rate over the past few years, so is the amount of data increasing exponentially on the internet. Due to this people find it difficult to choose the exact information they are looking for , learners find it difficult to suggest users exactly what they require. Here comes Recommendation Systems into picture to guide users towards the information according to their preferences. In context of Recommendation of Movies and TV shows on Online Streaming platforms ,this paper is aimed to explain making and implementation of Movie Recommendation Systems Using Machine Learning Algorithms, Sentiment Analysis and Cosine Similarity
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