Abstract
A Twitter user can easily be overwhelmed by flooding tweets from her followees, making it challenging for the user to find interesting and useful information in tweets. The feature of Twitter Lists allows users to organize their followees into multiple subsets for selectively digesting tweets. However, this feature has not received wide reception because users are reluctant to invest initial efforts in manually creating lists. To address the challenge of bootstrapping Twitter Lists, we envision a novel tool that automatically creates personalized Twitter Lists and recommends them to users. Compared with lists created by real Twitter users, the lists generated by our algorithms achieve 73.6% similarity.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Belém, F., Martins, E., Pontes, T., Almeida, J., Gonçalves, M.: Associative tag recommendation exploiting multiple textual features. In: SIGIR (2011)
Carbonell, J., Goldstein, J.: The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: SIGIR (1998)
Deng, H., Lyu, M.R., King, I.: A generalized co-hits algorithm and its application to bipartite graphs. In: SIGKDD (2009)
Guan, Z., Wang, C., Bu, J., Chen, C., Yang, K., Cai, D., He, X.: Document recommendation in social tagging services. In: WWW (2010)
Guy, I., Zwerdling, N., Ronen, I., Carmel, D., Uziel, E.: Social media recommendation based on people and tags. In: SIGIR (2010)
Khudyak, A., Kurland, O.: Cluster-based fusion of retrieved lists. In: SIGIR (2011)
Lu, C., Hu, X., Chen, X., Park, J.-R., He, T., Li, Z.: The topic-perspective model for social tagging systems. In: SIGKDD (2010)
Rowe, M., Wagner, C., Strohmaier, M., Alani, H.: Measuring the topical specificity of online communities. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 472–486. Springer, Heidelberg (2013)
Meeder, B., Karrer, B., Sayedi, A., Ravi, R., Borgs, C., Chayes, J.: We know who you followed last summer: inferring social link creation times in twitter. In: WWW (2011)
Otsu, N.: A threshold selection method from gray-level histograms. In: IEEE TSMC (1979)
Pennacchiotti, M., Popescu, A.-M.: Democrats, republicans and starbucks afficionados: user classification in twitter. In: SIGKDD (2011)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM (1975)
Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.: Personalized recommendation in social tagging systems using hierarchical clustering. In: RecSys (2008)
Venetis, P., Koutrika, G., Garcia-Molina, H.: On the selection of tags for tag clouds. In: WSDM (2011)
Yin, Z., Li, R., Mei, Q., Han, J.: Exploring social tagging graph for web object classification. In: SIGKDD (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, L., Zhao, Y., Chen, S., Fang, H., Li, C., Wang, M. (2013). iPLUG: Personalized List Recommendation in Twitter. In: Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (eds) Web Information Systems Engineering – WISE 2013. WISE 2013. Lecture Notes in Computer Science, vol 8181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41154-0_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-41154-0_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41153-3
Online ISBN: 978-3-642-41154-0
eBook Packages: Computer ScienceComputer Science (R0)