Abstract
User modeling based on the user-generated content of users on social networks such as Twitter has been studied widely, and has been used to provide personalized recommendations via inferred user interest profiles. Most previous studies have focused on active users who actively post tweets, and the corresponding inferred user interest profiles are generated by analyzing these users’ tweets. However, there are also a great number of passive users who only consume information from Twitter but do not post any tweets. In this paper, we propose a user modeling approach using the biographies (i.e., self descriptions in Twitter profiles) of a user’s followees (i.e., the accounts that they follow) to infer user interest profiles for passive users. We evaluate our user modeling strategy in the context of a link recommender system on Twitter. Results show that exploring the biographies of a user’s followees improves the quality of user modeling significantly compared to two state-of-the-art approaches leveraging the names and tweets of followees.
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
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
The prefix dc denotes http://purl.org/dc/terms/.
- 10.
The prefix dbo denotes http://dbpedia.org/ontology/.
- 11.
- 12.
- 13.
References
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
Abel, F., Hauff, C., Houben, G.-J., Tao, K.: Leveraging user modeling on the social web with linked data. In: Brambilla, M., Tokuda, T., Tolksdorf, R. (eds.) ICWE 2012. LNCS, vol. 7387, pp. 378–385. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31753-8_31
Besel, C., Schlötterer, J., Granitzer, M.: Inferring semantic interest profiles from twitter followees: does twitter know better than your friends? In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, NY, USA, pp. 1152–1157. SAC 2016. ACM, New York (2016)
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)
Faralli, S., Stilo, G., Velardi, P.: Recommendation of microblog users based on hierarchical interest profiles. Soc. Netw. Anal. Min. 5(1), 1–23 (2015)
Flati, T., Vannella, D., Pasini, T., Navigli, R.: Two is bigger (and better) than one: the Wikipedia bitaxonomy project. In: ACL, vol. 1, pp. 945–955 (2014)
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, Heidelberg (2014). doi:10.1007/978-3-319-07443-6_8
Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S.: DBpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web J. 6(2), 167–195 (2015)
Michelson, M., Macskassy, S.A.: Discovering users’ topics of interest on twitter: a first look. In: Proceedings of the fourth workshop on Analytics for noisy unstructured text data, pp. 73–80. ACM (2010)
Mislove, A., Viswanath, B., Gummadi, K.P., Druschel, P.: You are who you know: inferring user profiles in online social networks. In: Proceedings of the third ACM international conference on Web search and data mining, pp. 251–260. ACM (2010)
Orlandi, F., Breslin, J., Passant, A.: Aggregated, interoperable and multi-domain user profiles for the social web. In: Proceedings of the 8th International Conference on Semantic Systems, pp. 41–48. ACM (2012)
Piao, G.: User modeling on twitter with WordNet Synsets and DBpedia concepts for personalized recommendations. In: The 25th ACM International Conference on Information and Knowledge Management. ACM (2016)
Piao, G., Breslin, J.G.: Analyzing aggregated semantics-enabled user modeling on Google+ and twitter for personalized link recommendations. In: User Modeling, Adaptation, and Personalization, pp. 105–109. ACM (2016)
Piao, G., Breslin, J.G.: Exploring dynamics and semantics of user interests for user modeling on twitter for link recommendations. In: 12th International Conference on Semantic Systems, pp. 81–88. ACM (2016)
Sheth, A., Kapanipathi, P.: Semantic filtering for social data. IEEE Internet Comput. 20(4), 74–78 (2016)
Siehndel, P., Kawase, R.: TwikiMe!: user profiles that make sense. In: Proceedings of the 2012th International Conference on Posters and Demonstrations Track-Volume 914, pp. 61–64. CEUR-WS.org (2012)
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, Heidelberg (2016). doi:10.1007/978-3-319-30671-1_35
Acknowledgments
This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (Insight Centre for Data Analytics).
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
Piao, G., Breslin, J.G. (2017). Inferring User Interests for Passive Users on Twitter by Leveraging Followee Biographies. 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_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-56608-5_10
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)