Effective Matrix Factorization for Online Rating Prediction

Recommender systems have been widely utilized by online merchants and online advertisers to promote their products in order to improve profits. By evaluating customer interests based on their purchase history and relating it to commodities for sale these retailers could excavate out products which are most likely to be chosen by a specific customer. In this case, online ratings given by customers are of great interest as they could reflect different levels of customers’ interest on different products. Collaborative Filtering (CF) approach is chosen by a large amount of web-based retailers for their recommender systems because CF operates on interactions between customers and products. In this paper, a major approach of CF, Matrix Factorization, is modified to give more accurate recommendations by predicting online ratings.

[1]  Michael R. Lyu,et al.  Effective missing data prediction for collaborative filtering , 2007, SIGIR.

[2]  Hyung Jun Ahn,et al.  A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem , 2008, Inf. Sci..

[3]  Hans-Peter Kriegel,et al.  Ieee Transactions on Knowledge and Data Engineering Probabilistic Memory-based Collaborative Filtering , 2022 .

[4]  John Riedl,et al.  Recommender Systems for Large-scale E-Commerce : Scalable Neighborhood Formation Using Clustering , 2002 .

[5]  G. Karypis,et al.  Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems , 2002 .

[6]  Fernando Díez,et al.  Simple time-biased KNN-based recommendations , 2010, CAMRa '10.

[7]  T. Ferguson.  An Inconsistent Maximum Likelihood Estimate , 1982 .

[8]  Debajyoti Mukhopadhyay,et al.  An Item-Based Collaborative Filtering using Dimensionality Reduction Techniques on Mahout Framework , 2015, ArXiv.

[9]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[10]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .

[11]  Arindam Banerjee,et al.  Generalized Probabilistic Matrix Factorizations for Collaborative Filtering , 2010, 2010 IEEE International Conference on Data Mining.

[12]  Amos Azaria,et al.  Movie recommender system for profit maximization , 2013, AAAI.

[13]  Jianying Hu,et al.  One-Class Matrix Completion with Low-Density Factorizations , 2010, 2010 IEEE International Conference on Data Mining.

[14]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[15]  Jun Wang,et al.  Unifying user-based and item-based collaborative filtering approaches by similarity fusion , 2006, SIGIR.

[16]  Zhenhua Wang,et al.  An improved collaborative movie recommendation system using computational intelligence , 2014, J. Vis. Lang. Comput..

[17]  Debajyoti Mukhopadhyay,et al.  Matrix Factorization Model in Collaborative Filtering Algorithms: A Survey , 2015 .

[18]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[19]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[20]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[21]  Dean P. Foster,et al.  Clustering Methods for Collaborative Filtering , 1998, AAAI 1998.

[22]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[23]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[24]  Yehuda Koren,et al.  Collaborative filtering with temporal dynamics , 2009, KDD.

[25]  Lars Schmidt-Thieme,et al.  MyMediaLite: a free recommender system library , 2011, RecSys '11.

[26]  Gerard Salton,et al.  Research and Development in Information Retrieval , 1982, Lecture Notes in Computer Science.

[27]  Hideki Asoh,et al.  A Context-Aware Movie Preference Model Using a Bayesian Network for Recommendation and Promotion , 2007, User Modeling.

[28]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[29]  Ted Hong,et al.  Use of KNN for the Netflix Prize , 2006 .

[30]  Peter J. Haas,et al.  Large-scale matrix factorization with distributed stochastic gradient descent , 2011, KDD.