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Journal articles on the topic 'Personalized Movie Recommendation'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

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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Chaitanya., G., Y. Hemanth., K. Koushik., P. Haneef., and Pranav.S. "Movie Recomondation System using Machine Learning and Spark." International Journal of Innovative Science and Research Technology (IJISRT) 8, no. 6 (2024): 6. https://doi.org/10.5281/zenodo.10686713.

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In this abstract, we present a cutting- edge movie recommendation system that combines the power of machine learning algorithms with the scalability and speed of the Spark framework. Our system is designed to deliver highly accurate and personalized movie recommendations to users by analyzing their viewing history, preferences, and demographic information. By leveraging Spark's distributed computing capabilities, we efficiently process large-scale movie datasets and train complex recommendation models in parallel. The results of our experiments demonstrate the system's superior recommendation

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Singh, Balraj, Aadil Shaikh, Priyanka Jadhav, Rajneesh Chaturvedi, and Prof Ankit Sanghvi. "Cinephiles Integration System using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 3349–53. http://dx.doi.org/10.22214/ijraset.2023.50947.

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Abstract: Our project aims to create a movie recommendation and community platform where users can discover and share their favorite movies with others. The platform will utilize a recommendation system to suggest personalized movie recommendations based on the user's preferences and viewing history. Users will also be able to rate and review movies, create watchlists, and follow other users with similar movie tastes. The community aspect of the platform will allow users to engage with others through forums, discussions, and private messaging. This will create a space for movie enthusiasts to

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B, Sreeja. "Movie Lens – Movie Recommendation System Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33379.

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Recommendation systems, the best way to deal with information overload, are widely utilized to provide users with personalized content and services with high efficiency. Many recommendation algorithms have been researched and deployed extensively in various e-commerce applications, including the movie streaming services over the last decade. However, sparse data cold-start problems are often encountered in many movie recommendation systems. In this paper, we reported a personalized multimodal movie recommendation system based on multimodal data analysis and deep learning. The real-world MovieL

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Yu, Saisai, Ming Guo, Xiangyong Chen, Jianlong Qiu, and Jianqiang Sun. "Personalized Movie Recommendations Based on a Multi-Feature Attention Mechanism with Neural Networks." Mathematics 11, no. 6 (2023): 1355. http://dx.doi.org/10.3390/math11061355.

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With the rapid growth of the Internet, a wealth of movie resources are readily available on the major search engines. Still, it is unlikely that users will be able to find precisely the movies they are more interested in any time soon. Traditional recommendation algorithms, such as collaborative filtering recommendation algorithms only use the user’s rating information of the movie, without using the attribute information of the user and the movie, which has the problem of inaccurate recommendations. In order to achieve personalized accurate movie recommendations, a movie recommendation algori

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Chandrasekaran, K. S., G. A. Varun, D. S. Sujjit, H. Subashree, and R. Thirumaalchelvan. "Movie Recommendation System Using Machine Learning Techniques." International Journal of Multidisciplinary Research Transactions 5, no. 7 (2023): 148–57. https://doi.org/10.5281/zenodo.7942135.

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Movie recommendation systems are becoming increasingly popular, with many businesses looking to leverage the power of data to personalize the user experience and improve customer engagement. Machine learning techniques are an effective way to analyse large datasets of user behavior and generate accurate and relevant recommendations. In this project, we propose a machine learning-based movie recommendation system that uses content-based filtering techniques to generate personalized recommendations for users. Our system takes into account the user's viewing history, ratings, and preferences,

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MD Rokibul Hasan and Janatul Ferdous. "Dominance of AI and Machine Learning Techniques in Hybrid Movie Recommendation System Applying Text-to-number Conversion and Cosine Similarity Approaches." Journal of Computer Science and Technology Studies 6, no. 1 (2024): 94–102. http://dx.doi.org/10.32996/jcsts.2024.6.1.10.

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This research explored movie recommendation systems based on predicting top-rated and suitable movies for users. This research proposed a hybrid movie recommendation system that integrates both text-to-number conversion and cosine similarity approaches to predict the most top-rated and desired movies for the targeted users. The proposed movie recommendation employed the Alternating Least Squares (ALS) algorithm to reinforce the accuracy of movie recommendations. The performance analysis and evaluation were undertaken by employing the widely used "TMDB 5000 Movie Dataset" from the Kaggle datase

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Li, Luyao, Hong Huang, Qianqian Li, and Junfeng Man. "Personalized movie recommendations based on deep representation learning." PeerJ Computer Science 9 (July 12, 2023): e1448. http://dx.doi.org/10.7717/peerj-cs.1448.

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Personalized recommendation is a technical means to help users quickly and efficiently obtain interesting content from massive information. However, the traditional recommendation algorithm is difficult to solve the problem of sparse data and cold-start and does not make reasonable use of the user-item rating matrix. In this article, a personalized recommendation method based on deep belief network (DBN) and softmax regression is proposed to address the issues with traditional recommendation algorithms. In this method, the DBN is used to learn the deep representation of users and items, and th

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GAYATRI, GADIKAR. "TOWARDS A HYBRID PERSONALIZED MOVIE RECOMMENDER SYSTEM." JournalNX - A Multidisciplinary Peer Reviewed Journal ICACTM (May 3, 2018): 71–74. https://doi.org/10.5281/zenodo.1410013.

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Recommender systems represent a powerful method for enabling users to filter through wide verity of information. Research in the recommender system is moving in the direction of a richer understanding of how recommender technology may be embedded in specific domains. A recommendation system for movies is important in our social life to provide the enhanced entertainment .There are two major recommendation techniques â€“Collaborative and content based filtering but these filtering techniques are having some limitations thus an hybrid approach is often adopted. The proposed movi

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Chinivar, Spoorthi. "Personalized Recommendations of Products to Users." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 3 (2022): 105–9. http://dx.doi.org/10.35940/ijrte.c7274.0911322.

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Many organizations utilize recommendation systems to increase their profitability and win over their customers, including Facebook, which suggests friends, LinkedIn, which promotes employment, Spotify, which recommends music, Netflix, which recommends movies, and Amazon, which recommends purchases. When it comes to movie recommendation system, suggestions are made based on user similarities (collaborative filtering) or by considering a specific user's behavior (content-based filtering) that he or she wishes to interact with. Using TF-IDF, cosine similarity method for content-based filtering, a

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Spoorthi, Chinivar. "Personalized Recommendations of Products to Users." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 3 (2022): 105–9. https://doi.org/10.35940/ijrte.C7274.0911322.

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<strong>Abstract:</strong> Many organizations utilize recommendation systems to increase their profitability and win over their customers, including Facebook, which suggests friends, LinkedIn, which promotes employment, Spotify, which recommends music, Netflix, which recommends movies, and Amazon, which recommends purchases. When it comes to movie recommendation system, suggestions are made based on user similarities (collaborative filtering) or by considering a specific user's behavior (content-based filtering) that he or she wishes to interact with. Using TF-IDF, cosine similarity method

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Zhang, Yinghe. "Movie Recommendation System Based on Machine Learning." Highlights in Business, Economics and Management 21 (December 12, 2023): 698–702. http://dx.doi.org/10.54097/hbem.v21i.14740.

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In today’s era, with the growth of businesses on an individual basis, many applications are trying to provide more effective services to individual users, and eventually launched personalized services. Simply put, when a user enters certain keywords in a search engine, the recommendation system can push certain information or products to the user. Based on this principle, there are personalized recommendation videos on the homepage of short video platforms, and various music software have launched personalized radio stations. Even if you want to watch a movie on Netflix, it can recommend it to

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Krishna, Vivek, and Manish Singh. "Personalized movie recommendation system: Tailoring cinematic suggestions." International Journal of Applied Research 4, no. 11 (2018): 306–15. http://dx.doi.org/10.22271/allresearch.2018.v4.i11d.11462.

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C, Ms DEVADHARSHINI. "Sentiment Analysis for Movie Recommendation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30356.

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Our research proposes a movie recommendation system integrating sentiment analysis with LSTM neural networks. By extracting sentiments from user reviews, LSTM captures nuanced preferences, enhancing recommendation accuracy. Leveraging real-world movie datasets, our approach outperforms existing methods, offering personalized recommendations aligned with user sentiments. Through this fusion of NLP and deep learning, we strive to streamline movie selection, providing users with a more tailored and satisfying experience. Keywords—Sentiment Analysis, Natural Language Processing,, Long Short-Term M

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Sheng, Zijie. "Research on Personalized Movie Recommendation System Based on Collaborative Filtering." Highlights in Science, Engineering and Technology 140 (May 23, 2025): 72–77. https://doi.org/10.54097/hhfgh748.

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It is becoming increasingly apparent that the wealth of Internet movie resources can, on occasion, be overwhelming. This has brought to the fore the importance of personalized movie recommendation systems. This paper explores such systems based on collaborative filtering. The MovieLens100k dataset is selected for analysis, and its source, scale, characteristics, user ratings, movies, and user information it contains are described in detail. We then constructed a collaborative filtering recommendation model and analyzed and compared the performance of different algorithms. The paper goes on to

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Mahajan, Manan, Abhedya Mishra, Praveen Kumar, and Ms Sapna Gupta. "Moviebox: A Movie Recommendation System." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 3410–14. http://dx.doi.org/10.22214/ijraset.2023.54258.

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Abstract: In the vast world of cinema, numerous critically acclaimed movies often go unnoticed, despite their significant artistic and cultural value. Recognizing this disparity, our research endeavors to bridge the gap by developing a comprehensive movie recommendation system that highlights these "Out of the box" films. In the initial phase, we meticulously collected a bespoke dataset by scraping data from IMDb, encompassing a wide range of movies from various genres and regions. In the subsequent phase, we constructed an advanced algorithm utilizing content-based filtering techniques. This

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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 (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

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Phan, Hong Thi Thu, Vuong Luong Nguyen, Trinh Quoc Vo, and Nguyen Ho Trong Pham. "Hybrid knowledge-infused collaborative filtering for enhanced movie clustering and recommendation." HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY 14, no. 1 (2024): 41–51. http://dx.doi.org/10.46223/hcmcoujs.tech.en.14.1.2927.2024.

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This article proposes an enhanced knowledge-based collaborative filtering model for movie recommendation services to address the limitations of collaborative filtering in capturing the diverse preferences and specific characteristics of movies. The proposed model integrates external knowledge sources, such as movie plots and reviews, to enrich the recommendation process. By leveraging this additional information, the model can better understand movies’ unique features and attributes, improving recommendation accuracy and relevance. The knowledge-based features are extracted and incorporated in

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Tennakoon, Nimasha, Oshada Senaweera, and H. A. S. G. Dharmarathne. "Emotion-Based Movie Recommendation System." International Journal on Advances in ICT for Emerging Regions (ICTer) 17, no. 1 (2024): 34–39. http://dx.doi.org/10.4038/icter.v17i1.7275.

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This study presents a novel approach for a movie recommendation system that uses the emotions of a user to recommend movies. To detect user emotions, the system uses both facial expressions and text analysis. To detect facial expressions, several types of pre-trained models were re-trained and evaluated using benchmark datasets (FER2013). The ResNet50 model which has the highest accuracy of 73% was selected as the final model. For text analysis, several classical machine learning models (SVM, RF, MNB) and deep learning models (LSTM, Bi-LSTM, BERT, BERT+CNN) were trained and evaluated for their

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Mu, Yongheng, and Yun Wu. "Multimodal Movie Recommendation System Using Deep Learning." Mathematics 11, no. 4 (2023): 895. http://dx.doi.org/10.3390/math11040895.

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Recommendation systems, the best way to deal with information overload, are widely utilized to provide users with personalized content and services with high efficiency. Many recommendation algorithms have been researched and deployed extensively in various e-commerce applications, including the movie streaming services over the last decade. However, sparse data cold-start problems are often encountered in many movie recommendation systems. In this paper, we reported a personalized multimodal movie recommendation system based on multimodal data analysis and deep learning. The real-world MovieL

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Harikumar, Varsha. "Movie Recommendation System with Fake Review Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 1535–42. http://dx.doi.org/10.22214/ijraset.2024.63355.

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Abstract: With the rapid development of network technology and entertainment creation, the diversity of movie genres has expanded significantly. Users often face the challenge of selecting movies that align with their preferences. Deep learning, a field that has garnered substantial attention from scholars, offers the potential to model more complex structures due to its deep architecture. This study proposes a personalized movie recommendation system integrating deep learning for enhanced accuracy and tailored suggestions. We combine personalized recommendation and collaborative filtering alg

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Debangan Mandal, Mohammad Aasim, Piyush Dirg, Rajashri Biswas, Rutvik Ramteke, and Manpreet Kaur. "Beyond the Hype : Building a Personalized Movie Experience with Content-Based Recommendation." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 3 (2024): 28–39. http://dx.doi.org/10.32628/cseit24102129.

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This review paper provides a detailed analysis of how a movie recommendation system based on content was planned, executed and evaluated using Streamlit framework. The exponential expansion of digital content has led to the need for efficient recommendation systems, particularly in the realm of movies. In this respect, the proposed recommender system utilizes cosine similarity computations and content-based filtering methods to offer personalized film suggestions relying upon various features such as genres, keywords, castings as well as crews. The author further illustrates rigorous data prep

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Nandan, Brij. "Movie Recommendation System using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35094.

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There is already enough content available on the movie recommendation system. Showing the movie recommendations is essential so that the user need not waste a lot of time searching for the content which he/she might like. Thus, movie recommendation system plays a vital role to get user personalized movie recommendations. After searching a lot on the internet and referring to a lot of research papers, we got to know that the recommendations made using Content-based Filtering are using a single text to vector conversion technique and a single technique to find the similarity between the vectors.

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Nandini, Mrs Kagita, Sreya Deepthi Puppala, Vadupu Varshita, Sudulagunta Ratna Megda, and Thommandru Sumanth. "Deep Hybrid System for Personalized Movie Recommendations." Journal of Nonlinear Analysis and Optimization 16, no. 01 (2025): 739–47. https://doi.org/10.36893/jnao.2025.v16i01.087.

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A proposal framework is a sort of programming application or calculation intended to recommend things, for example, items, films, melodies, articles to clients in light of their inclinations, ways of behaving, or comparable client's exercises. This paper presents an original crossover suggestion framework that coordinates content-based and cooperative separating approaches utilizing profound learning procedures to improve film proposals. Our model merges the metadata of movies, including genres, cast, and crew from the Movie Lens dataset with user ratings to construct a comprehensive feature s

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R, Rajasekar, Niranchana Radhakrishnan, Sridar K, et al. "Intelligent movie recommendation system." Salud, Ciencia y Tecnología - Serie de Conferencias 4 (March 12, 2025): 1438. https://doi.org/10.56294/sctconf20251438.

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A smart piece of technology, a movie recommendation system uses intricate algorithms to deliver users personalized movie recommendations, enhancing their viewing experience. By utilizing many data sources, including user ratings, movie metadata, and viewing history, the system creates comprehensive user profiles that encompass distinct interests and behaviors. Content-based filtering, which considers storyline keywords, character, and genre, suggests movies that are similar to the ones the buyer has already enjoyed. Collaborative filtering approaches enhance suggestions even further by identif

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Kumar, Abhishek. "Ethical Movie Recommender System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49531.

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ABSTRACT This research paper presents CineScope, a smart movie recommendation system utilizing artificial intelligence and voice recognition. Built with Python, Streamlit , and integrated with the TMDB API, the system provides intelligent, personalized movie recommendations based on content similarity and user interaction. With support for voice-based search, mood-driven design, trending/upcoming movie displays, and trailer previews. CineScope delivers an immersive user experience aimed at enhancing movie discovery.

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Sharma, Saurabh, Ghanshyam Prasad Dubey, and Harish Kumar Shaky. "Optimizing User Satisfaction in Movie Recommendations Using Variable Learning Rates and Dynamic Neighborhood Functions in SOMs." International Journal of Experimental Research and Review 41, Spl Vol (2024): 130–45. http://dx.doi.org/10.52756/ijerr.2024.v41spl.011.

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Customized movie recommendations are crucial in elevating user satisfaction and engagement in the era of vast online entertainment options. This study presents an innovative approach utilizing Enhanced Self-Organizing Maps (SOMs) for movie categorization. SOMs, as unsupervised neural networks, are highly effective in recommendation systems due to their ability to identify intricate data patterns accurately. The proposed method involves collecting user-movie interaction data, such as user ratings and movie attributes. Data standardization is performed to ensure consistency before training the r

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Zhanwar, Radhe, Shivaji Pawar, Atul Narkhede, and Kavita Venkatachari. "A Hybrid Movie Recommendation System Integrating Content-Based Filtering With Personality Traits." International Journal of Environmental Sciences 11, no. 9s (2025): 623–30. https://doi.org/10.64252/xkhkh891.

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This paper presents a novel approach to movie recommendations by integrating traditional content-based filtering with personality traits analysis. We propose a hybrid system that combines TF-IDF-based similarity measures with the Big Five personality model to generate personalized movie recommendations. Our system analyzes movie features, including genres, cast, crew, and keywords, while incorporating user personality traits to adjust recommendations. Experimental results demonstrate improved recommendation diversity and user satisfaction compared to traditional content-based approaches. The h

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Wang, Kai. "Research on movie recommendation algorithms based on machine learning." Applied and Computational Engineering 71, no. 1 (2024): 7–13. http://dx.doi.org/10.54254/2755-2721/71/20241656.

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In recent years, the number of movies released worldwide has grown exponentially. Due to the large number of movies, it is difficult for users to find movies that match their preferences. Therefore, with the development of Internet technology, it has become an important research direction how to filter out the movies that users are interested in from the massive movie data. This paper mainly focuses on the film recommendation algorithms based on machine learning, including the traditional collaborative filtering algorithm, rating-based sorting recommendation algorithm and content-based recomme

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Sankaran, Mahesh, and E. N. Ganesh. "Similarity based deep learning model for movie recommendation system." E3S Web of Conferences 389 (2023): 07024. http://dx.doi.org/10.1051/e3sconf/202338907024.

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“Movie Recommendation Systems” helps user get relative & relevant items within millions of items. “Movie recommendation system’s” main task is to offer personalized content through information filtering. Here through this paper, we want to develop Similarity Based Deep Learning Model (SDLM) for automatic movie recommendation system. The projected technique is developed to identify the best rated movies and automatic movie recommendation system. This SDLM is a combination of “Spiking Neural Network (SNN)” and “Ebola Optimization Search Algorithm (EOSA)”. In the SNN, the EOSA is utilized to

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Guo, Rui. "Enhancing Movie Recommendation Systems Through CNN-Based Feature Extraction and Optimized Collaborative Filtering." Applied and Computational Engineering 81, no. 1 (2024): 134–40. http://dx.doi.org/10.54254/2755-2721/81/20241080.

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Abstract. This paper presents the design and implementation of a movie recommendation model that combines collaborative filtering with Convolutional Neural Networks (CNNs) to tackle the challenge of information overload in extensive movie databases. The primary objective is to enhance the user experience by developing an efficient and personalized recommendation system. The proposed model integrates CNNs to extract detailed image features from movie posters, enriching the movie representations used in the recommendation process. This feature extraction is coupled with an optimized collaborativ

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Aditi, Aditi, Ghanshyam Prasad Dubey, Harish Kumar Shakya, and Aditi Sharma. "SOM and Hybrid Filtering: Pioneering Next-Gen Movie Recommendations in the Entertainment Industry." Fusion: Practice and Applications 16, no. 2 (2024): 43–62. http://dx.doi.org/10.54216/fpa.160204.

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In an age where digital connectivity is increasingly shaping entertainment content, personalized movie recommendations play a pivotal role in enhancing user satisfaction and engagement. This research introduces an innovative approach utilizing Enhanced Self-Organizing Maps (SOM) to streamline movie selection processes. Self-Organizing Maps (SOMs), a type of unsupervised neural network architecture, are particularly adept at discerning intricate data patterns, making them valuable assets in recommendation systems. The methodology outlined in this paper commences with gathering user-movie intera

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崔, 苹. "Personalized Movie Recommendation System Based on LDA Theme Extension." Computer Science and Application 08, no. 06 (2018): 860–66. http://dx.doi.org/10.12677/csa.2018.86095.

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Engineering, Mathematical Problems in. "Retracted: Personalized Movie Recommendation Method Based on Deep Learning." Mathematical Problems in Engineering 2023 (July 26, 2023): 1. http://dx.doi.org/10.1155/2023/9834934.

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K H, Latha. "Movie Recommendations Based on Emotions Using Web Scraping." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 3348–52. http://dx.doi.org/10.22214/ijraset.2022.45732.

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Abstract: A recommender system help the users to get personalized recommendations, helps users to take correct decisions in their online transactions, increase sales and redefine the users web browsing experience. The purpose of this project is to recommend a list of movies for a user based on a specific emotion to suit his/her needs and desires. This recommendation system can be achieved through web scraping. Web Scraping is an automatic method to obtain and extract content and large amounts of data from websites. This recommendation system can also provide insights to current movie trends an

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Dr. N. Rajeswari, Meduri Bhuvaneswari, Nadimpalli Krishna Sathwika, Gurana Anjali, and K H N Siddik. "PERSONALIZED RECOMMENDATION ON MOVIESMETADATA USING HYBRID APPROACH." Industrial Engineering Journal 54, no. 02 (2025): 90–98. https://doi.org/10.36893/iej.2025.v52i2.010.

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Recommendation systems are a form of data filtering that plays a crucial role in helping users discover relevant content,based on their interests and needs. They have become an integral feature of numerous platforms, particularly in digital streaming services, where users often struggle with decision fatigue due to vast content. However, challenges such as the coldstart problem, data sparsity, and evolving user preferences require advanced approaches that balance accuracy and scalability.This study presents a hybrid movie recommendation system that integrates content-based filtering and collab

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Tulsyan, Ansh, Anshul Bhardwaj, Pranjal Shukla, Jatin Verma, and Tushar Singh. "ONLINE PLATFORM FOR MOVIE RECOMMENDATION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40457.

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It might be difficult to locate movies that suit personal tastes in the era of digital media. A state-of-the-art internet tool called CineMatch tackles this problem by providing individualized movie suggestions using sophisticated algorithmic analysis. With the use of both user input and machine learning techniques, CineMatch offers a distinctive, user-focused movie-selection experience. The complex recommendation engine at the heart of CineMatch's technology combines filtering based on content, filtering that is collaborative, and the processing of natural language (NLP). The program can eval

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Kumar, Sachin. "INTELLIGENT MOVIE RECOMMENDER FRAMEWORK BASED ON CONTENT-BASED & COLLABORATIVE FILTERING ASSISTED WITH SENTIMENT ANALYSIS." International Journal of Advanced Research in Computer Science 14, no. 03 (2023): 108–13. http://dx.doi.org/10.26483/ijarcs.v14i3.6979.

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Recommendation systems for rating movies and forming opinion have grown exponentially in popularity, they make it very convenient for consumers to select films that suit their tastes. However, traditional recommendation systems often rely solely on user ratings or reviews, which may not accurately reflect the user's true feelings about a movie. To address this issue, sentiment analysis has been proposed as a more reliable method for capturing emotional information about movies. In this research paper, we propose a novel movie recommendation system that combines sentiment analysis with collabor

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ZERIKAT, Yacine, and Mokhtar ZERIKAT. "Movie Recommendation System Based on Machine Learning using Profiling." International Journal of Artificial Intelligence & Applications 16, no. 1 (2025): 59–68. https://doi.org/10.5121/ijaia.2025.16105.

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With the increasing amount of data available, recommendation systems are important for helping users find relevant content. This paper introduces a movie recommendation system that uses user profiles and machine learning techniques to improve the user experience by offering personalized suggestions. We tested different machine learning methods, including k nearest neighbors (KNN), support vector machines (SVM), and neural networks. We used several datasets, such as MovieLens and Netflix Prize, to check how accurate the recommendations were and how satisfied users were with them.

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AIRCC. "Movie Recommendation System Based on Machine Learning using Profiling." International Journal of Artificial Intelligence & Applications (IJAIA) 16, no. 1 (2025): 59–68. https://doi.org/10.5121/ijaia.2025.16105.

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With the increasing amount of data available, recommendation systems are important for helping users find relevant content. This paper introduces a movie recommendation system that uses user profiles and machine learning techniques to improve the user experience by offering personalized suggestions. We tested different machine learning methods, including k nearest neighbors (KNN), support vector machines (SVM), and neural networks. We used several datasets, such as MovieLens and Netflix Prize, to check how accurate the recommendations were and how satisfied users were with them.

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Tang, Ke. "Movie Recommendation System Based on Collaborative Filtering Network." Applied and Computational Engineering 96, no. 1 (2024): 113–19. http://dx.doi.org/10.54254/2755-2721/96/20241441.

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Abstract. In the era of big data, personalized recommendation systems have become crucial for enhancing user experience and improving information retrieval efficiency, especially in the movie recommendation domain. This study proposes a method based on Collaborative Filtering Network (CFN), utilizing deep learning techniques to construct an efficient and accurate movie recommendation system. We evaluated the model's effectiveness using the MovieLens dataset. The experimental results demonstrate that the proposed CFN model performs well across multiple metrics, including Hit Ratio, providing ne

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