Relevant bibliographies by topics / Personalized Movie Recommendation

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Personalized Movie Recommendation'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Personalized Movie Recommendation"

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H K, Shashikala, K Praghnya Iyer, Himaja K R, and Rahisha Pokharel. "Personalized Movie Recommendation System." International Journal of Information Technology, Research and Applications 2, no. 1 (2023): 1–6. http://dx.doi.org/10.59461/ijitra.v2i1.40.

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In the digital world of today, where there is an infinite amount of content to consume, including movies, books, videos, articles, and so on, finding content that appeals to one's tastes has become challenging. On the other hand, providers of digital content want to keep as many people using their service for as long as possible. This is where the recommender system comes into play, where content providers suggest content to users based on their preferences. Web applications that offer a variety of services and automatically suggest some services based on user interest increasingly rely on rec
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Shashikala, H.K, Iyer K. Praghnya, K.R Himaja, and Pokharel Rahisha. "Personalized Movie Recommendation System." International Journal of Information Technology, Research and Applications (IJITRA) ISSN: 2583 5343 2, no. 1 (2023): 1–6. https://doi.org/10.5281/zenodo.7779051.

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In the digital world of today, where there is an infinite amount of content to consume, including movies, books, videos, articles, and so on, finding content that appeals to one's tastes has become challenging. On the other hand, providers of digital content want to keep as many people using their service for as long as possible. This is where the recommender system comes into play, where content providers suggest content to users based on their preferences. Web applications that offer a variety of services and automatically suggest some services based on user interest increasingly rely on
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Sinha, Shweta, and Treya Sharma. "Content-Based Movie Recommendation System: An Enhanced Approach to Personalized Movie Recommendations." International Journal of Innovative Research in Computer Science and Technology 11, no. 3 (2023): 67–71. http://dx.doi.org/10.55524/ijircst.2023.11.3.12.

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With the exponential growth of digital media platforms and the vast amount of available movie content, users are often overwhelmed when selecting movies that match their preferences. Recommender systems have emerged as an effective solution to assist users in discovering relevant and enjoyable movies. Among these systems, content-based recommendation approaches have gained popularity due to their ability to recommend items based on the content characteristics of movies, such as genres, actors, directors, and plot summaries. The first stage of our system involves the collection and preprocessin
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Shweta, Sinha, and Sharma Treya. "Content-Based Movie Recommendation System: An Enhanced Approach to Personalized Movie Recommendations." International Journal of Innovative Research in Computer Science and Technology (IJIRCST) 11, no. 03 (2023): 67–71. https://doi.org/10.5281/zenodo.8113691.

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With the exponential growth of digital media platforms and the vast amount of available movie content, users are often overwhelmed when selecting movies that match their preferences. Recommender systems have emerged as an effective solution to assist users in discovering relevant and enjoyable movies. Among these systems, content-based recommendation approaches have gained popularity due to their ability to recommend items based on the content characteristics of movies, such as genres, actors, directors, and plot summaries. The first stage of our system involves the collection and preprocessin
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SUHAIB, MD. "RECOMMENDAITON SYSTEM ENGINE FOR MOVIES USING MACHINE LEARNING ALGORITHM (TF-IDF VECTORIZATION)." International Scientific Journal of Engineering and Management 03, no. 04 (2024): 1–9. http://dx.doi.org/10.55041/isjem01577.

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By offering tailored movie recommendations, Movie Recommendation Systems (MRS) are crucial for improving the user experience on streaming services. This research paper proposes and evaluates a Movie Recommendation System utilizing TF-IDF vectorization and cosine similarity. TF-IDF vectorization is used to analyze textual information related to movies, such as plot summaries, cast bios, and genres, in order to give users precise and pertinent suggestions. The similarity between the user's preferences and the movies in the dataset is then calculated using cosine similarity. The results of the st
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Bhat, Dr Vandana Shreenivas, Anish Joshi, Basavaprabhu, Darshan Bentur, Shreejit Kundargi, and Samhitharaj Bali. "Movie Recommendation System." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 694–95. https://doi.org/10.22214/ijraset.2025.66299.

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Abstract: The Movie Recommendation System is designed to provide personalized movie suggestions using techniques like collaborative filtering, content-based filtering, and hybrid models. By analyzing user ratings, preferences, and movie metadata, the system generates accurate recommendations. Built using Python and machine learning libraries like Scikit-learn and TensorFlow, it ensures continuous improvement through dynamic updates. The project focuses on efficient algorithm implementation, intuitive user interface design, and performance evaluation using metrics like precision and RMSE, with
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Wu, Yuetong. "Movie Recommendation System Using KNN, Cosine Similarity and Collaborative Filtering." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 339–46. http://dx.doi.org/10.54097/bz63hm80.

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The movie recommendation system is becoming increasingly popular in the digital era. With the continuous emergence of a vast amount of movie resources, users are facing more and more choices. Therefore, an intelligent movie recommendation system can assist users in quickly finding movies that match their personal preferences, thereby enhancing user satisfaction and movie-watching experience . Our movie recommendation system recommends high-rated and well-reviewed films and TV shows to the general audience based on Netflix viewers’ ratings for them. These are the movies and shows that are consi
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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 field
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Sreemukhi, Adluri, Mekapothula Chandu Goud, Gade Tushitha Reddy, Nelli Sreevidya, and Subhani Shaik. "Natural Language Processing Based on Movie Rating System Using Microblogging." Asian Journal of Research in Computer Science 18, no. 6 (2025): 9–18. https://doi.org/10.9734/ajrcos/2025/v18i6676.

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Movie recommendation systems help users quickly find movies that match their preferences, similar to platforms like Netflix, which personalize suggestions based on individual viewing habits. As digital content grows exponentially with technological advancements, users face challenges in discovering movies that align with their taste, sentiment, and genre. To address this issue, various software solutions have been developed to improve movie recommendations. However, traditional recommendation methods, such as content-based and collaborative filtering, often struggle to deliver highly personali
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Zhang, Haocheng, Yanxia Zhao, Xinyang Pan, Jiale Fu, and Xuanyin Yao. "Personalized Movie Recommendation based on Convolutional Neural Network." Scientific Journal of Technology 6, no. 11 (2024): 68–84. http://dx.doi.org/10.54691/5x7vxb68.

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In pace with the development of the economy, the spiritual entertainment brought by movies is increasingly valued by people, and the problem of how to recommend the most suitable movie for users among the numerous movies also arises. Based on this, experiments were conducted using convolutional neural networks in the field of deep learning for movie recommendation. The convolutional neural network was trained using user information, movie information, and user movie rating data from the Douban Movie Network. In data preprocessing, instead of converting category fields to one hot encoding, they
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Dissertations / Theses on the topic "Personalized Movie Recommendation"

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SINGH, YOGENDRA. "A PERSONALIZED HYBRID MOVIE RECOMMENDATION SYSTEM FOR USERS." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15145.

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We describe a rating logical thinking approach to incorporating matter user reviews into Collaborative Filtering (CF) algorithms. The main motive of our approach is to use user preferences which is expressed in movie reviews and then convert such user’s preferences into some rating that may be understood by existing CF algorithms. The linguistics score of subjective sentence is fetched from SentiWordNet Library to calculate their sentiments as +ve, -ve or neutral based on the textual review. We’ve used SentiWordNet library as a dataset with two completely different approaches of alternatives c
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Book chapters on the topic "Personalized Movie Recommendation"

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

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Das, Debashis, Himadri Tanaya Chidananda, and Laxman Sahoo. "Personalized Movie Recommendation System Using Twitter Data." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7871-2_33.

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Bahi, Abderaouf, Ibtissem Gasmi, and Sassi Bentrad. "Personalized Movie Recommendation Prediction Using Reinforcement Learning." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43838-7_4.

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Teppalwar, Vansh, Kanhu Charan Sahoo, R. C. Jaiswal, and Mousami V. Munot. "A Survey on Personalized Movie Recommendation System Using Machine Learning." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1320-2_25.

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Sivakumar, Aishwarya, Nidheesha Amedapu, Vasudha Avuthu, and M. Brindha. "Personalised Structure Balance Theory-Based Movie Recommendation System." In Evolution in Computational Intelligence. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5788-0_5.

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Ciuciu, Ioana, and Yan Tang. "A Personalized and Collaborative eLearning Materials Recommendation Scenario Using Ontology-Based Data Matching Strategies." In On the Move to Meaningful Internet Systems: OTM 2010 Workshops. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16961-8_81.

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Dewangan, Deepak Kumar. "A Graph Neural Network Approach to Personalized Movie Recommendations Through Link Prediction in Graph-Based Data." In Synthesis Lectures on Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-93802-3_8.

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Behera, Anukampa, Chhabi Rani Panigrahi, Abhishek Mishra, Bibudhendu Pati, and Sumit Mitra. "Movie Recommendations." In Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815136746123010009.

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Recently, most retail-based and e-commerce companies have been using recommender systems aggressively. It retains a customer's interest by giving exclusive offers on personalized preferences. The primary purpose of a recommender system is to get at an increase in sales by providing an enriched experience to the customer. With the emergence of many video streaming services like Netflix, Hotstar, and amazon prime video, the dependency on movie recommendation systems has increased. It facilitates the users in faster search and easier access for shows matching their tastes and helps them choose wh
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Codina Victor and Ceccaroni Luigi. "Taking Advantage of Semantics in Recommendation Systems." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-643-0-163.

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Recommendation systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommendation systems, content-based recommendation systems and a few hybrid systems. We propose a semantic framework to overcome common limitations of current systems. We present a system whose representations of items and user-profiles are based on concept taxonomies in order to provide personalized recommendation and services. The recommender incorporates semantics to enhance (1) user modeling by applying a domain-based inference method, and (2) recomm
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Bathrinath S., Saranyadevi S., Thirumalai Kumaran S., and Saravanasankar S. "PageRank Algorithm-Based Recommender System Using Uniformly Average Rating Matrix." In Handbook of Research on Green Engineering Techniques for Modern Manufacturing. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5445-5.ch006.

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Applications of web data mining is the prediction of user behavior with respect to items. Recommender systems are being applied in knowledge discovery techniques to the problem of making decisions on personalized recommendation of information. Traditional CF approaches involve the amount of effort increases with number of users. Hence, new recommender systems need to be developed to process high quality recommendations for large-scale networks. In this chapter, a model for UAR matrix construction method for item rank calculations, a Page Rank-based item ranking approach are proposed. The analy
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Conference papers on the topic "Personalized Movie Recommendation"

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Vali-Sarafoglou, Evrydiki, and Konstantina Chrysafiadi. "TopMoviePicks: A Personalized Movie Recommendation System Based on TOPSIS." In 2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA). IEEE, 2024. https://doi.org/10.1109/iisa62523.2024.10786675.

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Nithya, B., Asha V, Sreeja S. P, Abhay Aditya Sinha, Akash P, and Akshay Bharadwaj S. "Personalized Content-based Movie Recommendation System: A Comparative Analysis." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961557.

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Jain, Jitender, Yogesh Ramaswamy, Leeladhar Gudala, Rami Ryad Hossein, and G. Sri Satya. "Personalized Movie Recommendation System based on Proximal Policy Optimization." In 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE, 2025. https://doi.org/10.1109/icdsis65355.2025.11071087.

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Padthe, Adithya, Saef Wbaid, Ramesh Babu N, Rohith Vallabhaneni, and Challaraj Emmanuel E S. "Personalized Movie Recommendation System based on Proximal Policy Optimization." In 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE, 2025. https://doi.org/10.1109/icdsis65355.2025.11070626.

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N, Ravikumar R., Sanjay Jain, and Manash Sarkar. "Personalized Movie Recommendation Based on User Preferences Using Optimized Sequential Transformer Model." In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit). IEEE, 2024. https://doi.org/10.1109/globalaisummit62156.2024.10947887.

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D, Shyam Prakash, Abirami S, Muthuraman P, Sathish C, and Sundharabalaji K. L. "Personalized Movie Recommendations: A Comprehensive Review and Analysis." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10716924.

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Vashisht, Rohit, Rahul Kumar Sharma, Maneesh Pant, Gagan Thakral, and Prashant Naresh. "A Content-Based Filtering Approach for Personalized Movie Recommendations." In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). IEEE, 2025. https://doi.org/10.1109/cictn64563.2025.10932540.

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

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Wang, Lin. "Personalized Movie Recommendation Based on Social Tagging Systems." In 2017 7th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2017). Atlantis Press, 2017. http://dx.doi.org/10.2991/icadme-17.2017.78.

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Xiong, Wei, and Chengwan He. "Personalized Movie Hybrid Recommendation Model Based on GRU." In 2021 4th International Conference on Robotics, Control and Automation Engineering (RCAE). IEEE, 2021. http://dx.doi.org/10.1109/rcae53607.2021.9638949.

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