INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Movie recommender systems play a crucial role in modern media consumption, aiding users in discovering new content tailored to their preferences. Collaborative filtering (CF) techniques, which leverage user-item interaction data to make recommendations, have gained significant attention due to their effectiveness in various domains, including movie recommendation. This paper presents a comprehensive review of collaborative filtering-based movie recommender systems, focusing on their methodologies, algorithms, evaluation metrics, and challenges. We explore different types of collaborative filtering approaches, including memory-based and model-based methods, highlighting their strengths and limitations. Furthermore, we discuss recent advancements such as hybrid approaches, deep learning-based CF models, and the incorporation of contextual information to enhance recommendation accuracy and diversity. Evaluation metrics and benchmark datasets commonly used in assessing the performance of movie recommender systems are also examined. Finally, we address challenges and future directions in the field, including scalability issues, cold-start problems, and the integration of explainability in recommendation systems.
Keywords:
Collaborative filtering ,Recommendation ,Scalability ,Memory based ,Model based
Cite Article:
"Movie Recommender System ", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.d192-d197, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404324.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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