Abstract In Flickr,one of the most popular photo service websites,groups are superior for photos′ propagation and they can gather photos of similar themes,which brings convenience to users′ easy browsing. Therefore,many researchers are studying how to help users to find out which groups they may be interested in. In this paper,a probabilistic matrix factorization (PMF)based model is proposed for Flickr group recommendation by employing the information of users′ contacts and tags in group. The complexity analysis indicates that the proposed model is efficient and it can be applied to large datasets. The experimental results on a Flickr dataset show the effectiveness of the proposed model. Finally,a Flickr group recommendation system is developed based on the proposed model.
[1] Lerman K.,Jones L. Social Browsing on Flickr[EB/OL] .[2012-01-03] .http://icwsm. org/paper/3-Lerman-Jones.pdf [2] Zheng Nan,Li Qiudan,Liao Shengcai,et al. Flickr Group Recommendation Based on Tensor Decomposition // Proc of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Geneva,Switzerland,2010: 737-738 [3] Zheng Nan,Li Qiudan,Liao Shengcai,et al. Which Photo Groups Should I Choose? A Comparative Study of Recommendation Algorithms in Flickr. Journal of Information Science,2010,36(6): 733-750 [4] Ma Hao,Yang Haixuan,Lyu M R,et al. SoRec-Social Recommendation Using Probabilistic Matrix Factorization // Proc of the 17th Conference on Information and Knowledge Management. Napa Valley,USA,2008: 931-940 [5] Ma Hao,King I,Lyu M R. Learning to Recommend with Social Trust Ensemble // Proc of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Boston,USA,2009: 203-210 [6] Ma Haixuan,Lyu M R,King I. Learning to Recommend with Trust and Distrust Relationships // Proc of the 3rd ACM Conference on Recommender Systems. New York,USA,2009: 189-196 [7] Ma Hao,Zhou Dengyong,Liu Chao,et al. Recommender Systems with Social Regularization // Proc of the 4th ACM International Conference on Web Search and Data Mining. Hong Kong,China,2011: 287-296 [8] Jamali M,Ester M. A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks // Proc of the 4th ACM Conference on Recommender Systems. Barcelona,Spain,2010: 135-142 [9] Golder S,Huberman B A. Usage Patterns of Collaborative Tagging Systems. Journal of Information Science,2006,32(2): 198-208 [10] Sigurbjrnsson B,van Zwol R. Flickr Tag Recommendation Based on Collective Knowledge // Proc of the 17th International Conference on World Wide Web. Beijing,China,2008: 327-336 [11] Garg N,Weber I. Personalized Tag Suggestion for Flickr // Proc of the 17th International Conference on World Wide Web. Beijing,China,2008: 1063-1064 [12] Jaschke R,Marinho L,Hotho A,et al. Tag Recommendations in Folksonomies // Proc of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases. Warsaw,Poland,2007: 506-514 [13] Koren Y. Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model // Proc of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas,USA,2008: 426-434 [14] Zheng Nan,Li Qiudan. A Topic-Oriented Photo Browsing Method in Flickr Group. Pattern Recognition and Artificial Intelligence,2011,24(2): 272-276(in Chinese) (郑 楠,李秋丹.一种面向主题的Flickr组内照片浏览方法. 模式识别与人工智能,2011,24(2): 272-276)
2013, Vol. 26 
