Recommending Flickr groups with social topic model

The explosion of multimedia content in social media networks raises a great demand of developing tools to facilitate producing, sharing and viewing media content. Flickr groups, self-organized communities with declared common interests, are able to help users to conveniently participate in social media network. In this paper, we address the problem of automatically recommending groups to users. We propose to simultaneously exploit media contents and link structures between users and groups. To this end, we present a probabilistic latent topic model to model them in an integrated framework, expecting to jointly discover the latent interests for users and groups and simultaneously learn the recommendation function. We demonstrate the proposed approach on the dataset crawled from Flickr.com.

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

[2]  Qi Tian,et al.  Semi-automatic Flickr Group Suggestion , 2011, MMM.

[3]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[4]  Edward Y. Chang,et al.  Combinational collaborative filtering for personalized community recommendation , 2008, KDD.

[5]  Ja-Ling Wu,et al.  SheepDog: group and tag recommendation for flickr photos by automatic search-based learning , 2008, ACM Multimedia.

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

[7]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[8]  Jun S. Liu,et al.  Monte Carlo strategies in scientific computing , 2001 .

[9]  Alessandro Chessa,et al.  Group Recommendation with Automatic Identification of Users Communities , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[10]  Qiudan Li,et al.  Exploiting Semantic Hierarchies for Flickr Group , 2010, AMT.

[11]  Barry Smyth,et al.  Case-Based Group Recommendation: Compromising for Success , 2007, ICCBR.

[12]  Guillermo Jiménez-Díaz,et al.  Personality aware recommendations to groups , 2009, RecSys '09.

[13]  Steffen Staab,et al.  Exploiting Flickr Tags and Groups for Finding Landmark Photos , 2009, ECIR.

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

[15]  Andrew Zisserman,et al.  Efficient Visual Search of Videos Cast as Text Retrieval , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Shengcai Liao,et al.  Flickr group recommendation based on tensor decomposition , 2010, SIGIR.

[17]  Jiebo Luo,et al.  Mining Personal Image Collection for Social Group Suggestion , 2009, 2009 IEEE International Conference on Data Mining Workshops.

[18]  Daniel Gatica-Perez,et al.  Topickr: flickr groups and users reloaded , 2008, ACM Multimedia.

[19]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[20]  Thomas Hofmann,et al.  Probabilistic latent semantic indexing , 1999, SIGIR '99.

[21]  Yehuda Koren,et al.  Advances in Collaborative Filtering , 2011, Recommender Systems Handbook.

[22]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[23]  Dinh Q. Phung,et al.  Flickr hypergroups , 2009, ACM Multimedia.

[24]  Nenghai Yu,et al.  WWW 2009 MADRID! Track: Rich Media / Session: Tagging and Clustering Learning to , 2022 .

[25]  Jiebo Luo,et al.  Connecting people in photo-sharing sites by photo content and user annotations , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[26]  Daniel Gatica-Perez,et al.  Analyzing Flickr groups , 2008, CIVR '08.

[27]  Shengcai Liao,et al.  Which photo groups should I choose? A comparative study of recommendation algorithms in Flickr , 2010, J. Inf. Sci..

[28]  Yehuda Koren,et al.  Factor in the neighbors: Scalable and accurate collaborative filtering , 2010, TKDD.

[29]  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.

[30]  Thomas Hofmann,et al.  Latent semantic models for collaborative filtering , 2004, TOIS.

[31]  Daniel Gatica-Perez,et al.  Modeling Flickr Communities Through Probabilistic Topic-Based Analysis , 2010, IEEE Transactions on Multimedia.

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

[33]  Yoram Singer,et al.  An Efficient Boosting Algorithm for Combining Preferences by , 2013 .