Abstract
In this paper, we address the problem of predicting future interests of users with regards to a set of unobserved topics in microblogging services which enables forward planning based on potential future interests. Existing works in the literature that operate based on a known interest space cannot be directly applied to solve this problem. Such methods require at least a minimum user interaction with the topic to perform prediction. To tackle this problem, we integrate the semantic information derived from the Wikipedia category structure and the temporal evolution of user’s interests into our prediction model. More specifically, to capture the temporal behaviour of the topics and user’s interests, we consider discrete intervals and build user’s topic profile in each time interval separately. Then, we generalize users’ interests that have been observed over several time intervals by transferring them over the Wikipedia category structure. Our approach not only allows us to generalize users’ interests but also enables us to transfer users’ interests across different time intervals that do not necessarily have the same set of topics. Our experiments illustrate the superiority of our model compared to the state of the art.
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
Notes
References
Abel, F., Gao, Q., Houben, G., Tao, K.: Analyzing temporal dynamics in Twitter profiles for personalized recommendations in the social web. In: WebSci 2011, pp. 2:1–2:8 (2011)
Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Analyzing user modeling on Twitter for personalized news recommendations. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 1–12. Springer, Heidelberg (2011). doi:10.1007/978-3-642-22362-4_1
Bao, H., Li, Q., Liao, S.S., Song, S., Gao, H.: A new temporal and social pmf-based method to predict users’ interests in micro-blogging. Decis. Support Syst. 55(3), 698–709 (2013)
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)
Cheekula, S.K., Kapanipathi, P., Doran, D., Jain, P., Sheth, A.P.: Entity recommendations using hierarchical knowledge bases. In: ESWC 2015 (2015)
Cheng, X., Yan, X., Lan, Y., Guo, J.: BTM: topic modeling over short texts. IEEE Trans. Knowl. Data Eng. 26(12), 2928–2941 (2014)
Dahleh, M., Dahleh, M.A., Verghese, G.: Lectures on dynamic systems and control. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (2013)
Ding, Y., Li, X.: Time weight collaborative filtering. In: International Conference on Information and Knowledge Management, pp. 485–492 (2005)
Kapanipathi, P., Jain, P., Venkataramani, C., Sheth, A.: User interests identification on Twitter using a hierarchical knowledge base. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 99–113. Springer, Cham (2014). doi:10.1007/978-3-319-07443-6_8
Kapanipathi, P., Orlandi, F., Sheth, A.P., Passant, A.: Personalized filtering of the Twitter stream. In: The Second Workshop on Semantic Personalized Information Management: Retrieval and Recommendation 2011, pp. 6–13 (2011)
Khrouf, H., Troncy, R.: Hybrid event recommendation using linked data and user diversity. In: RecSys 2013, pp. 185–192 (2013)
Li, L., Zheng, L., Yang, F., Li, T.: Modeling and broadening temporal user interest in personalized news recommendation. Expert Syst. Appl. 41(7), 3168–3177 (2014)
Michelson, M., Macskassy,S.A.: Discovering users’ topics of interest on Twitter: a first look. In: AND 2010, pp. 73–80 (2010)
Noia, T.D., Mirizzi, R., Ostuni, V.C., Romito, D., Zanker, M.: Linked open data to support content-based recommender systems. In: I-SEMANTICS 2012, pp. 1–8 (2012)
Piao, G., Breslin, J.G.: Exploring dynamics and semantics of user interests for user modeling on Twitter for link recommendations. In: SEMANTICS 2016, pp. 81–88 (2016)
Varga, A., Basave, A.E.C., Rowe, M., Ciravegna, F., He, Y.: Linked knowledge sources for topic classification of microposts: a semantic graph-based approach. J. Web Semant. 26, 36–57 (2014)
Weng, J., Lim, E., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential Twitterers. In: WSDM 2010, pp. 261–270 (2010)
Weng, L., Xu, Y., Li, Y., Nayak, R.: Exploiting item taxonomy for solving cold-start problem in recommendation making. In: ICTAI 2008, pp. 113–120 (2008)
Xu, Z., Lu, R., Xiang, L., Yang,Q.: Discovering user interest on Twitter with a modified author-topic model. In: WI 2011, pp. 422–429 (2011)
Yang, L., Sun, T., Zhang, M., Mei, Q.: WWW 2012, pp. 261–270 (2012)
Yin, H., Cui, B., Chen, L., Hu, Z., Zhou, X.: Dynamic user modeling in social media systems. ACM Trans. Inf. Syst. 33(3), 10:1–10:44 (2015)
Yu, Y., Wang, C., Gao, Y.: Attributes coupling based item enhanced matrix factorization technique for recommender systems. CoRR, abs/1405.0770 (2014)
Zarrinkalam, F., Fani, H., Bagheri, E., Kahani, M.: Inferring implicit topical interests on Twitter. In: Ferro, N., Crestani, F., Moens, M.-F., Mothe, J., Silvestri, F., Nunzio, G.M., Hauff, C., Silvello, G. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 479–491. Springer, Cham (2016). doi:10.1007/978-3-319-30671-1_35
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zarrinkalam, F., Fani, H., Bagheri, E., Kahani, M. (2017). Predicting Users’ Future Interests on Twitter. In: Jose, J., et al. Advances in Information Retrieval. ECIR 2017. Lecture Notes in Computer Science(), vol 10193. Springer, Cham. https://doi.org/10.1007/978-3-319-56608-5_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-56608-5_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56607-8
Online ISBN: 978-3-319-56608-5
eBook Packages: Computer ScienceComputer Science (R0)