Procedia Computer Science · 2015 · 278 citations · 17 references
Group RecommendersCollaborative Filtering AlgorithmsMachine LearningData ScienceData MiningInformation RetrievalEngineeringMatrix FactorizationKnowledge DiscoveryComputer ScienceCollaborative FilteringRecommendation SystemCold-start ProblemRecommendation SystemsText MiningInformation Filtering SystemAbstract Recommendation Systems
Recommendation systems increasingly rely on collaborative filtering, which uses past user behavior to infer preferences, but face sparsity and scalability challenges that matrix factorization techniques aim to address. This paper surveys matrix factorization models—SVD, PCA, and PMF—to guide research and practice in collaborative filtering. The authors review and compare SVD, PCA, and PMF approaches, highlighting their mathematical foundations and applicability to large, sparse rating data. The survey demonstrates that matrix factorization effectively mitigates sparsity and scalability issues, offering a roadmap for future research and practical deployment.
Abstract Recommendation Systems (RSs) are becoming tools of choice to select the online information relevant to a given user. Collaborative Filtering (CF) is the most popular approach to build Recommendation System and has been successfully employed in many applications. Collaborative Filtering algorithms are much explored technique in the field of Data Mining and Information Retrieval. In CF, past user behavior are analyzed in order to establish connections between users and items to recommend an item to a user based on opinions of other users. Those customers, who had similar likings in the past, will have similar likings in the future. In the past decades due to the rapid growth of Internet usage, vast amount of data is generated and it has becomea challenge for CF algorithms. So, CF faces issues with sparsity of rating matrix and growing nature of data. These challenges are well taken care of by Matrix Factorization (MF). In this paper we are going to discuss different Matrix Factorization models such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Probabilistic Matrix Factorization (PMF). This paper attempts to present a comprehensive survey of MF model like SVD to address the challenges of CF algorithms, which can be served as a roadmap for research and practice in this area.
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Matrix Factorization Techniques for Recommender Systems
Yehuda Koren, Robert Bell, Chris Volinsky · Computer · 2009 · 11.4K citations
Engineering, Machine Learning, Matrix Factorization Models +17
Item-based collaborative filtering recommendation algorithms
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Factorization meets the neighborhood
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