Abstract
As the development of social media, the services in social media have significantly changed people’s habits of using Internet. However, as the large amount of information posted by users and the highly frequent updates in social media, users often face the problem of information overload and miss out of content that they may be interested in. Recommender systems, which recommends an item (e.g., a product, a service and a twitter etc.) to users based on their interests, is an effective technique to handle this issue. In this paper, we borrow matrix factorization model from recommender system to predict users’ behaviors of retweeting in social media. Compared with previous works, we take the relevance of users’ interests, tweets’ content, and publishers’ influence into account simultaneously. Our experimental results on a real-world dataset show that the proposed model achieves desirable performance in characterizing users’ retweeting behaviors and predicting topic diffusion in social media.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, K., Chen, T., Zheng, G., Jin, O., Yao, E., Yu, Y.: Collaborative personalized tweet recommendation. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 661–670. ACM (2012)
Hong, L., Doumith, A.S., Davison, B.D.: Co-factorization machines: modeling user interests and predicting individual decisions in twitter. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 557–566. ACM (2013)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Sun, A.R., Cheng, J., Zeng, D.D.: A novel recommendation framework for micro-blogging based on information diffusion. In: 19th Annual Workshop on Information Technolgies and Systems, (WITS 2009) (2009)
Chen, J., Nairn, R., Nelson, L., Bernstein, M., Chi, E.: Short and tweet: experiments on recommending content from information streams. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1185–1194. ACM (2010)
Duan, Y., Jiang, L., Qin, T., Zhou, M., Shum, H.Y.: An empirical study on learning to rank of tweets. In: Proceedings of the 23rd International Conference on Computational Linguistics, Association for Computational Linguistics, pp. 295–303 (2010)
Naveed, N., Gottron, T., Kunegis, J., Alhadi, A.C.: Bad news travel fast: A content-based analysis of interestingness on twitter. In: Proceedings of the 3rd International Web Science Conference, p. 8. ACM (2011)
Yang, Z., Guo, J., Cai, K., Tang, J., Li, J., Zhang, L., Su, Z.: Understanding retweeting behaviors in social networks. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1633–1636. ACM (2010)
Kim, Y., Shim, K.: Twitobi: A recommendation system for twitter using probabilistic modeling. In: 2011 IEEE 11th International Conference on Data Mining (ICDM), pp. 340–349. IEEE (2011)
Lin, C.X., Mei, Q., Han, J., Jiang, Y., Danilevsky, M.: The joint inference of topic diffusion and evolution in social communities. In: 2011 IEEE 11th International Conference on Data Mining (ICDM), pp. 378–387. IEEE (2011)
Koren, Y.: Factorization meets the neighborhood: A multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434. ACM (2008)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM (2001)
Shi, Y., Larson, M., Hanjalic, A.: Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 285–295. ACM (2009)
Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. (TIST) 3(3), 57 (2012)
Kywe, S.M., Lim, E.-P., Zhu, F.: A survey of recommender systems in twitter. In: Aberer, K., Flache, A., Jager, W., Liu, L., Tang, J., Guéret, C. (eds.) SocInfo 2012. LNCS, vol. 7710, pp. 420–433. Springer, Heidelberg (2012)
Acknowledgements
This work is supported by National Natural Science Foundation of China (Grant NO. 61300137), the Guangdong Natural Science Foundation, China (NO. S2013010013836), Science and Technology Planning Project of Guangdong Province, China (NO. 2013B010406004), the Fundamental Research Funds for the Central Universities, SCUT(NO. 2014ZZ0035).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Li, J., Qin, J., Wang, T., Cai, Y., Min, H. (2015). A Collaborative Filtering Model for Personalized Retweeting Prediction. In: Liu, A., Ishikawa, Y., Qian, T., Nutanong, S., Cheema, M. (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture Notes in Computer Science(), vol 9052. Springer, Cham. https://doi.org/10.1007/978-3-319-22324-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-22324-7_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22323-0
Online ISBN: 978-3-319-22324-7
eBook Packages: Computer ScienceComputer Science (R0)