Information Retrieval

, Volume 15, Issue 3–4, pp 278–295 | Cite as

Recommending Flickr groups with social topic model

  • Jingdong Wang
  • Zhe Zhao
  • Jiazhen Zhou
  • Hao Wang
  • Bin Cui
  • Guojun Qi
Information Retrieval for Social Media


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 group Recommendation Social topic model 



Bin Cui is supported by the grant of Natural Science Foundation of China (No. 61073019 and 60933004).


  1. Abbasi, R., Chernov, S., Nejdl, W., Paiu, R., & Staab, S. (2009). Exploiting flickr tags and groups for finding landmark photos. In ECIR, (pp. 654–661).Google Scholar
  2. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.CrossRefGoogle Scholar
  3. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.MATHGoogle Scholar
  4. Boratto, L., Carta, S., Chessa, A., Agelli, M., & Clemente, M. L. (2009). Group recommendation with automatic identification of users communities. In Web Intelligence/IAT Workshops, (pp. 547–550).Google Scholar
  5. Cai, J., Zha, Z. J., Tian, Q., & Wang, Z. (2011). Semi-automatic flickr group suggestion. In MMM (2), (pp. 77–87).Google Scholar
  6. Chen, H. M., Chang, M. H., Chang, P. C., Tien, M. C., Hsu, W. H. & Wu, J. L. (2008). Sheepdog: Group and tag recommendation for flickr photos by automatic search-based learning. In ACM Multimedia, (pp. 737–740).Google Scholar
  7. Chen, W., Zhang, D., & Chang, E. Y. (2008). Combinational collaborative filtering for personalized community recommendation. In KDD, (pp. 115–123).Google Scholar
  8. Deerwester, S. C., Dumais, S. T., Landauer, T. K., Furnas, G. W., & Harshman, R. A. (1990). Indexing by latent semantic analysis. JASIS, 41(6), 391–407.CrossRefGoogle Scholar
  9. Freund, Y., Iyer, R. D., Schapire, R. E., & Singer, Y. (2003). An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4, 933–969.MathSciNetGoogle Scholar
  10. Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 5–53.CrossRefGoogle Scholar
  11. Hofmann, T. (1999). Probabilistic latent semantic indexing. In SIGIR, (pp. 50–57).Google Scholar
  12. Hofmann, T. (2004). Latent semantic models for collaborative filtering. ACM Transactions on Information Systems, 22(1), 89–115.CrossRefGoogle Scholar
  13. Koren, Y. (2008). Factorization meets the neighborhood: A multifaceted collaborative filtering model. In KDD, (pp. 426–434).Google Scholar
  14. Koren, Y. (2010). Factor in the neighbors: Scalable and accurate collaborative filtering. TKDD, 4(1).Google Scholar
  15. Koren, Y., & Bell, R. M. (2011). Advances in collaborative filtering. In Recommender Systems Handbook (pp. 145–186).Google Scholar
  16. Liu, J. S. (2002). Monte Carlo strategies in scientific computing. Berlin: Springer.Google Scholar
  17. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.CrossRefGoogle Scholar
  18. Lu, D., & Li, Q. (2010). Exploiting semantic hierarchies for flickr group. In AMT, (pp. 74–85).Google Scholar
  19. Ma, H., King, I., & Lyu, M. R. (2009). Learning to recommend with social trust ensemble. In SIGIR, (pp. 203–210).Google Scholar
  20. Ma, H., Yang, H., Lyu, M. R., & King, I. (2008). Sorec: Social recommendation using probabilistic matrix factorization. In CIKM, (pp. 931–940).Google Scholar
  21. McCarthy, K., McGinty, L., & Smyth, B. (2007). Case-based group recommendation: Compromising for success. In ICCBR, (pp. 299–313).Google Scholar
  22. Negoescu, R. A., Adams, B., Phung, D. Q., Venkatesh, S., & Gatica-Perez, D. (2009). Flickr hypergroups. In ACM Multimedia, (pp. 813–816).Google Scholar
  23. Negoescu, R. A., & Gatica-Perez, D. (2008a). Analyzing flickr groups. In CIVR, (pp. 417–426).Google Scholar
  24. Negoescu, R. A., & Gatica-Perez, D. (2008b). Topickr: Flickr groups and users reloaded. In ACM Multimedia, (pp. 857–860).Google Scholar
  25. Negoescu, R. A., & Gatica-Perez, D. (2010). Modeling flickr communities through probabilistic topic-based analysis. IEEE Transactions on Multimedia, 12(5), 399–416.CrossRefGoogle Scholar
  26. Recio-García, J. A., Jiménez-Díaz, G., Sánchez-Ruiz-Granados, A. A., & Díaz-Agudo, B. (2009). Personality aware recommendations to groups. In RecSys, (pp. 325–328).Google Scholar
  27. Sivic, J., & Zisserman, A. (2009). Efficient visual search of videos cast as text retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(4), 591–606.CrossRefGoogle Scholar
  28. Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in Artificial Intellegence, 2009, 19.
  29. Wu, L., Yang, L., Yu, N., & Hua, X. S. (2009). Learning to tag. In WWW, (pp. 361–370).Google Scholar
  30. Yu, J., Jin, X., Han, J., & Luo, J. (2009a). Mining personal image collection for social group suggestion. In ICDM Workshops, (pp. 202–207).Google Scholar
  31. Yu, J., Joshi, D., & Luo, J. (2009b). Connecting people in photo-sharing sites by photo content and user annotations. In ICME, (pp. 1464–1467).Google Scholar
  32. Zheng, N., Li, Q., Liao, S., & Zhang, L. (2010a). Flickr group recommendation based on tensor decomposition. In SIGIR, (pp. 737–738).Google Scholar
  33. Zheng, N., Li, Q., Liao, S., & Zhang, L. (2010b). Which photo groups should i choose? a comparative study of recommendation algorithms in flickr. Journal of Information Science, 36(6), 733–750.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Jingdong Wang
    • 1
  • Zhe Zhao
    • 2
  • Jiazhen Zhou
    • 3
  • Hao Wang
    • 4
  • Bin Cui
    • 5
    • 6
  • Guojun Qi
    • 7
  1. 1.Microsoft Research AsiaHaidianPeople’s Republic of China
  2. 2.Electrical Engineering and Computer ScienceUniversity of MichiganAnn ArborUSA
  3. 3.Department of Computer SciencePeking UniversityBeijingPeople’s Republic of China
  4. 4.Beijing University of Posts and TelecommunicationsBeijingPeople’s Republic of China
  5. 5.State Key Laboratory of Virtual Reality Technology and SystemsBeihang UniversityBeijingPeople’s Republic of China
  6. 6.School of EECSPeking UniversityBeijingPeople’s Republic of China
  7. 7.Beckman InstituteUniversity of Illinois at Urbana-ChampaignUrbanaUSA

Personalised recommendations