FeatureMF – A Novel Collaborative Filtering Recommendation Model

This paper1 presents a novel matrix factorization (MF) model, called FeatureMF, which takes into account item features and thus addresses the cold-start item and data sparsity problems of collaborative filtering (CF). More specifically, the model extends item latent vectors with item representation learned from metadata. Experiments conducted on a public dataset with two testing views confirm that FeatureMF achieves better recommendation performance than some of the popular state-of-the-art MF models.

[1]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[2]  Zoran Obradovic,et al.  Collaborative Filtering Using a Regression-Based Approach , 2003, Knowledge and Information Systems.

[3]  Martha Larson,et al.  Collaborative Filtering beyond the User-Item Matrix , 2014, ACM Comput. Surv..

[4]  Ivan Ganchev,et al.  A trust-enriched approach for item-based collaborative filtering recommendations , 2016, 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP).

[5]  Geoffrey E. Hinton,et al.  Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.

[6]  Neil Yorke-Smith,et al.  A Novel Evidence-Based Bayesian Similarity Measure for Recommender Systems , 2016, ACM Trans. Web.

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

[8]  Hui Tian,et al.  A new user similarity model to improve the accuracy of collaborative filtering , 2014, Knowl. Based Syst..

[9]  Xing Xie,et al.  Collaborative location and activity recommendations with GPS history data , 2010, WWW '10.

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

[11]  Jin-guang Sun,et al.  A Neighborhood-based Matrix Factorization Technique for Recommendation , 2015 .

[12]  M. Jalili,et al.  Evaluating Collaborative Filtering Recommender Algorithms: A Survey , 2018, IEEE Access.

[13]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[14]  Ivan Ganchev,et al.  Exploiting User Feedbacks in Matrix Factorization for Recommender Systems , 2017, MEDI.

[15]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

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

[17]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[18]  Jie Zhang,et al.  TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation , 2014, AAAI.

[19]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[20]  Ignacio Fernández-Tobías.  Matrix factorization models for cross-domain recommendation : Addressing the cold start in collaborative filtering , 2017 .

[21]  Iván Cantador,et al.  Exploiting Social Tags in Matrix Factorization Models for Cross-domain Collaborative Filtering , 2014, CBRecSys@RecSys.

[22]  Michael R. Lyu,et al.  Learning to recommend with social trust ensemble , 2009, SIGIR.

[23]  Iván Cantador,et al.  Alleviating the new user problem in collaborative filtering by exploiting personality information , 2016, User Modeling and User-Adapted Interaction.

[24]  Siguang Chen,et al.  Hybrid Location-based Recommender System for Mobility and Travel Planning , 2019, Mobile Networks and Applications.

[25]  Alejandro Bellogín,et al.  A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews , 2018, User Modeling and User-Adapted Interaction.

[26]  Lars Schmidt-Thieme,et al.  Factorization models for context-/time-aware movie recommendations , 2010 .

[27]  Konstantinos G. Margaritis,et al.  Using SVD and demographic data for the enhancement of generalized Collaborative Filtering , 2007, Inf. Sci..

[28]  LiuHaifeng,et al.  A new user similarity model to improve the accuracy of collaborative filtering , 2014 .

[29]  George Karypis,et al.  A Comprehensive Survey of Neighborhood-based Recommendation Methods , 2011, Recommender Systems Handbook.

[30]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[31]  Neil Yorke-Smith,et al.  A Novel Recommendation Model Regularized with User Trust and Item Ratings , 2016, IEEE Transactions on Knowledge and Data Engineering.

[32]  Zibin Zheng,et al.  Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization , 2013, IEEE Transactions on Services Computing.

[33]  Philip S. Yu,et al.  Semantic Path based Personalized Recommendation on Weighted Heterogeneous Information Networks , 2015, CIKM.

[34]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

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

[36]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

[37]  Marcelo G. Manzato.  gSVD++: supporting implicit feedback on recommender systems with metadata awareness , 2013, SAC '13.

[38]  Daniel Thalmann,et al.  Merging trust in collaborative filtering to alleviate data sparsity and cold start , 2014, Knowl. Based Syst..

[39]  Martin Ester,et al.  A matrix factorization technique with trust propagation for recommendation in social networks , 2010, RecSys '10.

[40]  Arkadiusz Paterek,et al.  Improving regularized singular value decomposition for collaborative filtering , 2007 .

[41]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[42]  Ruslan Salakhutdinov,et al.  Bayesian probabilistic matrix factorization using Markov chain Monte Carlo , 2008, ICML '08.