User interest dynamics on personalized recommendation

Abstract Four real recommender system datasets, the Netflix, SMovieLens, LMovieLens and RYM datasets, are analyzed to gain an insight into their user interest characteristics. A preference of active users to cold objects and a diverse interest of inactive users are revealed, which characteristics are introduced to improve the personalized recommendation algorithms. Based on seven different algorithms, we propose a general improvement formula for them, and finally four new algorithms are obtained. Tested on the four datasets, all the new algorithms are found to outperform the original ones in recommendation accuracy, diversity and novelty, except for the diversity and novelty compared with a heat conduction algorithm. And the recommendation accuracy for the cold objects, referring to the objects with small degrees, is also improved. Moreover, one of the new algorithms shows better performance than two other excellent algorithms in many aspects, i.e., the hybrid algorithm of heat conduction and mass diffusion, and the biased heat conduction algorithm. Our work may shed a new light on personalized recommendation from the perspective of connecting empirical study with algorithm design.

[1]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[2]  Guang Chen,et al.  Alleviating bias leads to accurate and personalized recommendation , 2013 .

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

[4]  Linyuan Lu,et al.  Information filtering via preferential diffusion , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  P. Jaccard,et al.  Etude comparative de la distribution florale dans une portion des Alpes et des Jura , 1901 .

[6]  Yi-Cheng Zhang,et al.  Collaborative filtering with diffusion-based similarity on tripartite graphs , 2009, ArXiv.

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

[8]  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.

[9]  Luis Martínez,et al.  Fuzzy Tools in Recommender Systems: A Survey , 2017, Int. J. Comput. Intell. Syst..

[10]  Yi-Cheng Zhang,et al.  Effect of initial configuration on network-based recommendation , 2007, 0711.2506.

[11]  Yi-Cheng Zhang,et al.  Bipartite network projection and personal recommendation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Qiang Guo,et al.  Heat conduction information filtering via local information of bipartite networks , 2012, The European Physical Journal B.

[13]  Qiang Guo,et al.  Ultra-accurate collaborative information filtering via directed user similarity , 2014, ArXiv.

[14]  Lu Yu,et al.  TIIREC: A tensor approach for tag-driven item recommendation with sparse user generated content , 2017, Inf. Sci..

[15]  Fei Yu,et al.  Network-based recommendation algorithms: A review , 2015, Physica A: Statistical Mechanics and its Applications.

[16]  Yan Fu,et al.  Effect of recent popularity on heat-conduction based recommendation models , 2017 .

[17]  Dan Frankowski,et al.  Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.

[18]  Yi-Cheng Zhang,et al.  Information filtering via self-consistent refinement , 2008, 0802.3748.

[19]  Yi-Cheng Zhang,et al.  Information filtering via Iterative Refinement , 2006, ArXiv.

[20]  Wei-Xing Zhou,et al.  Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm , 2012 .

[21]  Yi-Cheng Zhang,et al.  Adaptive model for recommendation of news , 2009, ArXiv.

[22]  Ronald Rousseau,et al.  Similarity measures in scientometric research: The Jaccard index versus Salton's cosine formula , 1989, Inf. Process. Manag..

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

[24]  Rahul Katarya,et al.  Recent developments in affective recommender systems , 2016 .

[25]  Qiang Guo,et al.  Information filtering via biased heat conduction , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  Bela Gipp,et al.  Research-paper recommender systems: a literature survey , 2015, International Journal on Digital Libraries.

[27]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[28]  Yi-Cheng Zhang,et al.  Empirical analysis of web-based user-object bipartite networks , 2009, ArXiv.

[29]  Alexandre Vidmer,et al.  Information filtering by similarity-preferential diffusion processes , 2014 .

[30]  Matús Medo.  Network-based information filtering algorithms: ranking and recommendation , 2012, ArXiv.

[31]  Qiang Guo,et al.  Ceiling effect of online user interests for the movies , 2014 .

[32]  Yi-Cheng Zhang,et al.  Heat conduction process on community networks as a recommendation model. , 2007, Physical review letters.