A Personalized Interest-Forgetting Markov Model for Recommendations

Intelligent item recommendation is a key issue in AI research which enables recommender systems to be more "human-minded" when generating recommendations. However, one of the major features of human — forgetting, has barely been discussed as regards recommender systems. In this paper, we considered people's forgetting of interest when performing personalized recommendations, and brought forward a personalized framework to integrate interest-forgetting property with Markov model. Multiple implementations of the framework were investigated and compared. The experimental evaluation showed that our methods could significantly improve the accuracy of item recommendation, which verified the importance of considering interest-forgetting in recommendations.

[1]  Nicholas R. Jennings,et al.  An On-Line Algorithm for Semantic Forgetting , 2011, IJCAI.

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

[3]  G. R. Knecht,et al.  Costing, Technological Growth and Generalized Learning Curves , 1974 .

[4]  J. E. Mazur,et al.  Learning as accumulation: a reexamination of the learning curve. , 1978, Psychological bulletin.

[5]  Jianyong Wang,et al.  Personalizing Web Page Recommendation via Collaborative Filtering and Topic-Aware Markov Model , 2010, 2010 IEEE International Conference on Data Mining.

[6]  Maurice Bonney,et al.  A comparative study of learning curves with forgetting , 1997 .

[7]  Flávio Sanson Fogliatto,et al.  Learning curve models and applications: Literature review and research directions , 2011 .

[8]  David S. Rosenblum,et al.  Context-aware mobile music recommendation for daily activities , 2012, ACM Multimedia.

[9]  Jimeng Sun,et al.  Temporal recommendation on graphs via long- and short-term preference fusion , 2010, KDD.

[10]  Òscar Celma,et al.  Music Recommendation and Discovery - The Long Tail, Long Fail, and Long Play in the Digital Music Space , 2010 .

[11]  Christos Dimitrakakis,et al.  Bayesian variable order Markov models , 2010, AISTATS.

[12]  Michael R. Lyu,et al.  Where You Like to Go Next: Successive Point-of-Interest Recommendation , 2013, IJCAI.

[13]  A. Raftery.  A model for high-order Markov chains , 1985 .

[14]  Armelle Brun,et al.  Skipping-Based Collaborative Recommendations inspired from Statistical Language Modeling , 2010 .

[15]  David A. Nembhard,et al.  An empirical comparison of forgetting models , 2001, IEEE Trans. Engineering Management.

[16]  Guy Shani,et al.  An MDP-Based Recommender System , 2002, J. Mach. Learn. Res..

[17]  Bangzuo Zhang,et al.  Collaborative Filtering Based on User's Drifting Interests , 2012 .

[18]  Sergei Vassilvitskii,et al.  The dynamics of repeat consumption , 2014, WWW.

[19]  Julie A. Adams,et al.  Filtering Data Based on Human-Inspired Forgetting , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Òscar Celma Herrada.  Music recommendation and discovery in the long tail , 2009 .

[21]  T. P. Wright,et al.  Factors affecting the cost of airplanes , 1936 .

[22]  Andrew Heathcote,et al.  The form of the forgetting curve and the fate of memories , 2011 .

[23]  Robin Burke,et al.  Context-aware music recommendation based on latenttopic sequential patterns , 2012, RecSys.

[24]  Clara E. Bussenius,et al.  Memory : A Contribution to Experimental Psychology , 2017 .

[25]  Taher H. Haveliwala.  Topic-sensitive PageRank , 2002, IEEE Trans. Knowl. Data Eng..

[26]  Ran El-Yaniv,et al.  On Prediction Using Variable Order Markov Models , 2004, J. Artif. Intell. Res..

[27]  Lars Schmidt-Thieme,et al.  Factorizing personalized Markov chains for next-basket recommendation , 2010, WWW '10.