A Novel Social Event Recommendation Method Based on Social and Collaborative Friendships

Many social network sites (SNSs) provide social event functions to facilitate user interactions. However, it is difficult for users to find interesting events among the huge number posted on such sites. In this paper, we investigate the problem and propose a social event recommendation method that exploits user's social and collaborative friendships to recommend events of interest. As events are one-and-only items, their ratings are not available until they are over. Hence, traditional recommendation methods are incapable of event recommendation because they need sufficient ratings to generate recommendations. Instead of using ratings, we analyze the behavior patterns of social network users to measure their social and collaborative friendships. The friendships are aggregated to identify the acquaintances of a user and events relevant to the preferences of the acquaintances and the user are recommended. The results of experiments show that the proposed method is effective and it outperforms many well-known recommendation methods.

[1]  Hinrich Schütze,et al.  Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.

[2]  Ralf Klamma,et al.  You Never Walk Alone: Recommending Academic Events Based on Social Network Analysis , 2009, Complex.

[3]  Chien Chin Chen,et al.  An effective recommendation method for cold start new users using trust and distrust networks , 2013, Inf. Sci..

[4]  John Riedl,et al.  Application of Dimensionality Reduction in Recommender System - A Case Study , 2000 .

[5]  Eng Chew,et al.  A Hybrid Recommendation Approach for One-and-Only Items , 2005, Australian Conference on Artificial Intelligence.

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

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

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

[9]  Jon M. Kleinberg,et al.  Group formation in large social networks: membership, growth, and evolution , 2006, KDD '06.

[10]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[11]  Danah Boyd,et al.  Social Network Sites: Definition, History, and Scholarship , 2007, J. Comput. Mediat. Commun..

[12]  Mark P. Graus,et al.  Understanding choice overload in recommender systems , 2010, RecSys '10.

[13]  Petra Perner,et al.  Advances in Data Mining , 2002, Lecture Notes in Computer Science.

[14]  Toon De Pessemier,et al.  A user-centric evaluation of recommender algorithms for an event recommendation system , 2011, RecSys 2011.

[15]  Elizabeth M. Daly,et al.  Effective event discovery: using location and social information for scoping event recommendations , 2011, RecSys '11.

[16]  Einat Minkov,et al.  Collaborative future event recommendation , 2010, CIKM.

[17]  Françoise Fessant,et al.  Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems , 2008, ICDM.

[18]  Johanna D. Moore,et al.  Evaluating information presentation strategies for spoken recommendations , 2007, RecSys '07.

[19]  Chris Cornelis,et al.  A Fuzzy Relational Approach to Event Recommendation , 2005, IICAI.

[20]  Shichao Zhang,et al.  AI 2005: Advances in Artificial Intelligence, 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, December 5-9, 2005, Proceedings , 2005, Australian Conference on Artificial Intelligence.

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