A collaborative filtering algorithm fusing user-based, item-based and social networks

The traditional collaborative filtering recommendation algorithm can be divided into the user-based and the item-based two methods, which only uses the information in the rating matrix. Because of the limitation of the information capacity they used, it is difficult to further improve the accuracy of the recommendation, and cold start problem also affects the normal operation of the recommendation system. This paper presented a collaborative filtering recommendation algorithm (UISA) fusing user-based, item-based and social networks data. The algorithm uses the data of the neighbor relations in social networks, calculating the users' friends not reflected in the rating matrix. At the same time, we can calculate the similarity between items by using the data of item text in social networks, mining similar items not reflected in the rating matrix. In this way, it can fundamentally expand available information capacity of the traditional filtering collaboration recommendation algorithms, improve the recommendation accuracy, alleviate cold start problem. Experimental results based on KDD CUP 2012 real data show that compared with the traditional collaborative filtering system, this system has obvious advantages in the recommendation accuracy and ease of cold start.

[1]  Jennifer Golbeck,et al.  Generating Predictive Movie Recommendations from Trust in Social Networks , 2006, iTrust.

[2]  Bradley N. Miller,et al.  Social Information Filtering : Algorithms for Automating “ Word of Mouth , ” , 2017 .

[3]  Georg Lausen,et al.  Analyzing Correlation between Trust and User Similarity in Online Communities , 2004, iTrust.

[4]  Chao Liu,et al.  Recommender systems with social regularization , 2011, WSDM '11.

[5]  Hong Joo Lee,et al.  Use of social network information to enhance collaborative filtering performance , 2010, Expert Syst. Appl..

[6]  Hao Ma,et al.  An experimental study on implicit social recommendation , 2013, SIGIR.

[7]  Luo Si,et al.  An automatic weighting scheme for collaborative filtering , 2004, SIGIR '04.

[8]  Paolo Avesani,et al.  Trust-aware recommender systems , 2007, RecSys '07.

[9]  Jun Wang,et al.  Unifying user-based and item-based collaborative filtering approaches by similarity fusion , 2006, SIGIR.

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

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

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

[13]  Michael R. Lyu,et al.  SoRec: social recommendation using probabilistic matrix factorization , 2008, CIKM '08.

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

[15]  Gediminas Adomavicius,et al.  Personalization technologies , 2005, Commun. ACM.

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

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

[18]  Le Wu,et al.  Leveraging tagging for neighborhood-aware probabilistic matrix factorization , 2012, CIKM.

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