Abstract
Twitter has emerged as one of the most powerful micro-blogging services for real-time sharing of information on the web. The large volume of posts in several topics is overwhelming to twitter users who might be interested in only few topics. To this end, we propose TRUPI, a personalized recommendation system for the timelines of twitter users where tweets are ranked by the user’s personal interests. The proposed system combines the user social features and interactions as well as the history of her tweets content to attain her interests. The system captures the users interests dynamically by modeling them as a time variant in different topics to accommodate the change of these interests over time. More specifically, we combine a set of machine learning and natural language processing techniques to analyze the topics of the various tweets posted on the user’s timeline and rank them based on her dynamically detected interests. Our extensive performance evaluation on a publicly available dataset demonstrates the effectiveness of TRUPI and shows that it outperforms the competitive state of the art by 25% on nDCG@25, and 14% on MAP.
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
Preview
Unable to display preview. Download preview PDF.
References
http://www.noslang.com/ . Internet Slang Dictionary & Translator (last accessed January 06, 2014)
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)
Ritter, A., Clark, S.: Twitter NLP Tools (2011), https://github.com/aritter/twitter_nlp (last accessed January 06, 2014)
Becker, H., Naaman, M., Gravano, L.: Beyond Trending Topics: Real-World Event Identification on Twitter. In: Procs. ICWSM 2011 (2011)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. The Journal of Machine Learnnig Research 3, 993–1022 (2003)
Chen, J., Nairn, R., Nelson, L., Bernstein, M., Chi, E.: Short and Tweet: Experiments on Recommending Content from Information Streams. In: CHI (2010)
Chen, K., Chen, T., Zheng, G., Jin, O., Yao, E., Yu, Y.: Collaborative Personalized Tweet Recommendation. In: Procs. of SIGIR 2012 (2012)
Cortes, C., Vapnik, V.: Support-Vector Networks. Machine Learning 20(3), 273–297 (1995)
Duan, Y., Jiang, L., Qin, T., Zhou, M., Shum, H.-Y.: An empirical study on learning to rank of tweets. In: COLING 2010 (2010)
Feng, W., Wang, J.: Retweet or Not?: Personalized Tweet Re-ranking. In: Procs. of WSDM 2013, pp. 577–586 (2013)
GNU Aspell (2011), http://aspell.net/ (last accessed January 06, 2014)
Godin, F., Slavkovikj, V., De Neve, W., Schrauwen, B., Van de Walle, R.: Using Topic Models for Twitter Hashtag Recommendation. In: Procs. of WWW 2013 Companion (2013)
Guo, Y., Kang, L., Shi, T.: Personalized Tweet Ranking Based on AHP: A Case Study of Micro-blogging Message Ranking in T.Sina. In: WI-IAT 2012 (2012)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explorations 11(1), 10–18 (2009)
Hannon, J., Bennett, M., Smyth, B.: Recommending twitter users to follow using content and collaborative filtering approaches. In: RecSys 2010 (2010)
Hannon, J., McCarthy, K., Smyth, B.: Finding Useful Users on Twitter: Twittomender the Followee Recommender. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 784–787. Springer, Heidelberg (2011)
Huffington Post’s Twitter Statistics, http://www.huffingtonpost.com/belle-beth-cooper/10-surprising-new-twitter_b_4387476.html (last accessed January 06, 2014)
Joachims, T.: Optimizing Search Engines Using Clickthrough Data. In: Procs. of KDD 2002, pp. 133–142 (2002)
Khater, S., Elmongui, H.G., Gracanin, D.: Tweets You Like: Personalized Tweets Recommendation based on Dynamic Users Interests. In: SocialInformatics 2014 (2014)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a Social Network or a News Media. In: Procs. of WWW 2010, pp. 591–600 (2010)
Kywe, S.M., Hoang, T.-A., Lim, E.-P., Zhu, F.: On Recommending Hashtags in Twitter Networks. In: Procs. of SocInfo 2012, pp. 337–350 (2012)
Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: DBpedia - A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia. Semantic Web Journal (2014)
Li, R., Wang, S., Deng, H., Wang, R., Chang, K.C.-C.: Towards Social User Profiling: Unified and Discriminative Influence Model for Inferring Home Locations. In: Procs. of KDD 2012, pp. 1023–1031 (2012)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space. In: ICLR 2013 Workshops (2013)
De Francisci Morales, G., Gionis, A., Lucchese, C.: From Chatter to Headlines: Harnessing the Real-time Web for Personalized News Recommendation. In: Procs. of WSDM 2012, pp. 153–162 (2012)
Pennacchiotti, M., Silvestri, F., Vahabi, H., Venturini, R.: Making Your Interests Follow You on Twitter. In: Procs. of CIKM 2012 (2012)
Robert Half Technology. Whistle - But Don’t tweet - While You Work (2009), http://rht.mediaroom.com/index.php?s=131&item=790 (last accessed January 06, 2014)
Salton, G., Buckley, C.: Term-weighting Approaches in Automatic Text Retrieval. Information Processing & Management 24(5), 513–523 (1988)
Santos, I., Miñambres-Marcos, I., Laorden, C., Galán-García, P., Santamaría-Ibirika, A., Bringas, P.G.: Twitter Content-Based Spam Filtering. In: Procs. of CISIS 2013, pp. 449–458 (2013)
The Open Directory Project, http://www.dmoz.org/ (last accessed January 06, 2014)
Twitter (2006). http://www.twitter.com/ (last accessed January 06, 2014)
Twitter REST API, https://dev.twitter.com/docs (last accessed January 06, 2014)
Twitter Usage, http://about.twitter.com/company (last accessed January 06, 2014)
UDI-TwitterCrawl-Aug2012 (2012), https://wiki.cites.illinois.edu/wiki/display/forward/Dataset-UDI-TwitterCrawl-Aug2012 (last accessed January 06, 2014)
Uysal, I., Croft, W.B.: User oriented tweet ranking: a filtering approach to microblogs. In: Procs. of CIKM 2011, pp. 2261–2264 (2011)
Wikipedia (2001), http://www.wikipedia.org/ (last accessed January 06, 2014)
WikiSynonyms, http://wikisynonyms.ipeirotis.com/ (last accessed January 06, 2014)
Yan, R., Lapata, M., Li, X.: Tweet Recommendation with Graph Co-ranking. In: Procs. of ACL 2012, pp. 516–525 (2012)
YouTube (2005), http://www.youtube.com/ (last accessed January 06, 2014)
Zangerle, E., Gassler, W., Specht, G.: Recommending#-Tags in Twitter. In: Procs. of SASWeb 2011, pp. 67–78 (2011)
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
Elmongui, H.G., Mansour, R., Morsy, H., Khater, S., El-Sharkasy, A., Ibrahim, R. (2015). TRUPI: Twitter Recommendation Based on Users’ Personal Interests. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9042. Springer, Cham. https://doi.org/10.1007/978-3-319-18117-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-18117-2_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18116-5
Online ISBN: 978-3-319-18117-2
eBook Packages: Computer ScienceComputer Science (R0)