Abstract
The boom of Flickr, a photo-sharing social tagging system, leads to a dramatical increasing of online social interactions. For example, it offers millions of groups for users to join in order to share photos and keep relations. However, the rapidly increasing amount of groups hampers users’ participation, thus it is necessary to suggest groups according to users’ preferences. By analyzing user-generated tags, one can explore users’ potential interests, and discover the latent topics of the corresponding groups. Furthermore, users’ behaviors are affected by their friends. Based on these intuitions, we propose a topic-based group recommendation model to predict users’ potential interests and conduct group recommendations based on tags and social relations. The proposed model provides a way to fuse tag information and social network structure to predict users’ future interests accurately. The experimental results on a real-world dataset demonstrate the effectiveness of the proposed model.
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References
Zheng, N., Li, Q.: A recommender system based on tag and time information for social tagging systems. Expert Systems with Applications 38(4), 4575–4587 (2011)
Ye, M., Liu, X., Lee, W.-C.: Exploring social influence for recommendation - A probabilistic generative model approach. In: International Conference on Research & Development on Information Retrieval (SIGIR 2012), pp. 671–680 (2012)
Yang, S.H., Long, B., Smola, A., Sadagopan, N., Zheng, Z., Zha, H.: Like like alike - Joint friendship and interest propagation in social networks. In: Proceedings of the 20th International Conference on World Wide Web (WWW), pp. 537–546. ACM, Hyderabad (2011)
Bogers, T., van den Bosch, A.: Recommending scientific articles using citeulike. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 287–290 (2008)
Kang, Y., Yu, N.: Soft-constraint based online LDA for community recommendation. In: Qiu, G., Lam, K.M., Kiya, H., Xue, X.-Y., Kuo, C.-C.J., Lew, M.S. (eds.) PCM 2010, Part II. LNCS, vol. 6298, pp. 494–505. Springer, Heidelberg (2010)
Krestel, R., Fankhauser, P.: Personalized topic-based tag recommendation. Neurocomputing 76, 61–70 (2012)
Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in folksonomies. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 506–514. Springer, Heidelberg (2007)
Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: A unified framework for providing recommendations in social tagging systems based on ternary semantic analysis. IEEE Transactions on Knowledge and Data Engineering 22(2), 179–192 (2010)
Zheng, N., Li, Q., Liao, S., Zhang, L.: Which photo groups should I choose? A comparative study of recommendation algorithms in Flickr. Journal of Information Science 36(6), 733–750 (2010)
Yin, D., Guo, S., Chidlovskii, B., Davison, B.D., Archambeau, C., Bouchard, G.: Connecting comments and tags: improved modeling of social tagging systems. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 547–556 (2013)
Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 203–210 (2009)
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 4th ACM Conference on Recommender Systems, pp. 135–142 (2010)
Guy, I., Zwerdling, N., Ronen, I., Carmel, D., Uziel, E.: Social media recommendation based on people and tags. In: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 194–201 (2010)
Konstas, I., Stathopoulos, V., Jose, J.: On social networks and collaborative recommendation. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 195–202 (2009)
Wang, J., Zhao, Z., Zhou, J., Wang, H., Cui, B., Qi, G.: Recommending Flickr groups with social topic model. Information Retrieval 15(3-4), 278–295 (2012)
Rosen-Zvi, M., Griffiths, T., Steyvers, M., Smyth, P.: The author-topic model for authors and documents. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, pp. 487–494. AUAI Press (2004)
Steyvers, M., Griffiths, T.: Probabilistic topic models. Latent Semantic Analysis: A Road to Meaning, Handbook of Latent Semantic Analysis 427(7), 424–440 (2007)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceeding of the 14th ACM SIGKDD, pp. 426–434 (2008)
Zhang, S., Wang, W., Ford, J., Makedon, F.: Learning from incomplete ratings using non-negative matrix factorization. In: 6th SIAM Conference on Data Mining, pp. 548–552 (2006)
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Zheng, N., Bao, H. (2013). Flickr Group Recommendation Based on User-Generated Tags and Social Relations via Topic Model. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_62
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DOI: https://doi.org/10.1007/978-3-642-39068-5_62
Publisher Name: Springer, Berlin, Heidelberg
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