Evaluating Prediction Accuracy for Collaborative Filtering Algorithms in Recommender Systems

Recommender systems are a relatively new technology that is commonly used by e-commerce websites and streaming services among others, to predict user opinion about products. This report studies two ...

[1]  Martin P. Robillard,et al.  Recommendation Systems for Software Engineering , 2010, IEEE Software.

[2]  John Riedl,et al.  Collaborative Filtering Recommender Systems , 2011, Found. Trends Hum. Comput. Interact..

[3]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[4]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[5]  F. Maxwell Harper,et al.  The MovieLens Datasets: History and Context , 2016, TIIS.

[6]  T. Chai,et al.  Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature , 2014 .

[7]  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.

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

[9]  John Riedl,et al.  Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit , 2011, RecSys '11.

[10]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[11]  Tomoharu Iwata,et al.  Modeling user behavior in recommender systems based on maximum entropy , 2007, WWW '07.

[12]  Yi-Cheng Zhang,et al.  Recommender Systems , 2012, ArXiv.

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

[14]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[15]  Alejandro Bellogín Recommender system performance evaluation and prediction: information retrieval perspective , 2012 .

[16]  Din J. Wasem,et al.  Mining of Massive Datasets , 2014 .

[17]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

[18]  A. B. Kouki,et al.  Recommender system performance evaluation and prediction an information retrieval perspective , 2014 .

[19]  Ling Liu,et al.  Encyclopedia of Database Systems , 2009, Encyclopedia of Database Systems.

[20]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[21]  Guy Shani,et al.  Evaluating Recommender Systems , 2015, Recommender Systems Handbook.

[22]  Gediminas Adomavicius,et al.  Integrating User Behavior and Collaborative Methods in Recommender Systems , 2004 .

[23]  Gediminas Adomavicius,et al.  Impact of data characteristics on recommender systems performance , 2012, TMIS.

[24]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

[25]  Yang Guo,et al.  A survey of collaborative filtering based social recommender systems , 2014, Comput. Commun..

[26]  W. Marsden I and J , 2012 .

[27]  Matthias Jarke,et al.  A Clustering Approach for Collaborative Filtering Recommendation Using Social Network Analysis , 2011, J. Univers. Comput. Sci..

[28]  Paul Resnick,et al.  Recommender systems , 1997, CACM.