Trees for explaining recommendations made through collaborative filtering

In this paper, we present a novel technique for explaining the recommendations made by recommender systems based on collaborative filtering. Our technique is based on the visualisation of trees of items, and it provides users with a quick and attractive way of understanding the recommendations. This type of visualisation provides users with valuable information about the reliability of the recommendations and the importance of the ratings the user has made, which may help users to decide which recommendation to choose.

[1]  Chris Cornelis,et al.  Gradual trust and distrust in recommender systems , 2009, Fuzzy Sets Syst..

[2]  Juan C. Burguillo,et al.  A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition , 2010, Inf. Sci..

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

[4]  Xin Jin,et al.  A maximum entropy web recommendation system: combining collaborative and content features , 2005, KDD '05.

[5]  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.

[6]  Alexis Papadimitriou,et al.  A generalized taxonomy of explanations styles for traditional and social recommender systems , 2012, Data Mining and Knowledge Discovery.

[7]  Fernando Ortega,et al.  Incorporating reliability measurements into the predictions of a recommender system , 2013, Inf. Sci..

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

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

[10]  Francisco Herrera,et al.  A web based consensus support system for group decision making problems and incomplete preferences , 2010, Inf. Sci..

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

[12]  Padhraic Smyth,et al.  KDD Cup and workshop 2007 , 2007, SKDD.

[13]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[14]  Judith Masthoff,et al.  A Survey of Explanations in Recommender Systems , 2007, 2007 IEEE 23rd International Conference on Data Engineering Workshop.

[15]  Yoon Ho Cho,et al.  Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations , 2010, Inf. Sci..

[16]  Nick Antonopoulos,et al.  CinemaScreen recommender agent: combining collaborative and content-based filtering , 2006, IEEE Intelligent Systems.

[17]  Kwei-Jay Lin,et al.  Building Web 2.0 , 2007, Computer.

[18]  Raymond J. Mooney,et al.  Explaining Recommendations: Satisfaction vs. Promotion , 2005 .

[19]  Panagiotis Symeonidis,et al.  Providing Justifications in Recommender Systems , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[20]  Yehuda Koren,et al.  Modeling relationships at multiple scales to improve accuracy of large recommender systems , 2007, KDD '07.

[21]  Fernando Ortega,et al.  Collaborative filtering based on significances , 2012, Inf. Sci..

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

[23]  Yehuda Koren,et al.  Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[24]  Jia Wang,et al.  User comments for news recommendation in forum-based social media , 2010, Inf. Sci..