Combining long-term and short-term user interest for personalized hashtag recommendation

Abstract

Hashtags, terms prefixed by a hash-symbol #, are widely used and inserted anywhere within short messages (tweets) on micro-blogging systems as they present rich sentiment information on topics that people are interested in. In this paper, we focus on the problem of hashtag recommendation considering their personalized and temporal aspects. As far as we know, this is the first work addressing this issue specially to recommend personalized hashtags combining longterm and short-term user interest.We introduce three features to capture personal and temporal user interest: 1) hashtag textual information; 2) user behavior; and 3) time. We offer two recommendation models for comparison: a linearcombined model, and an enhanced session-based temporal graph (STG) model, Topic-STG, considering the features to learn user preferences and subsequently recommend personalized hashtags. Experiments on two real tweet datasets illustrate the effectiveness of the proposed models and algorithms.

Keywords

recommendation / hashtag / time-sensitive / user interest

References

[1] Cui A Q, Zhang M, Liu Y Q, Ma S P, Zhang K. Discover breaking events with popular hashtags in twitter. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012, 1794―1798

[2] Zhang Y, Wu Y, Yang Q. Community discovery in twitter based on user interests. Journal of Computational Information Systems, 2012, 8(3): 991―1000

[3] Ding Z Y, Qiu X P, Zhang Q, Huan X J. Learning topical translation model for microblog hashtag suggestion. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2013, 2078―2084

[4] Hurley N, Zhang M. Novelty and diversity in top-n recommendation- analysis and evaluation. ACM Transactions on Internet Technology, 2011, 10(4): 14

[5] Huang J, Thornton K M, Efthimiadis E N. Conversational tagging in twitter. In: Proceedings of the 21st ACM Conference on Hyptertext and Hypermedia. 2010, 173―178

[6] Yu J J, Shen Y. Evolutionary personalized hashtag recommendation. In: Proceedings of the International Conference on Web-Age Information Management. 2014, 34―37

[7] Yu J J, Shen Y, Yang Z L. Temporal recommendation on graphs via long- and short-term preference fusion. In: Proceedings of the 23rd International World Wide Web Conference. 2014, 413―414

[8] Xiang L, Yuan Q, Zhao S W, Chen L, Zhang X T, Yang Q, Sun J M. Temporal recommendation on graphs via long- and short-term preference fusion. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and DataMining. 2010, 723―732

[9] Chen J L, 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. 2010, 1185―1194

[10] Hannon J, Bennett M, Smyth B. Recommending twitter users to follow using content and collaborative filtering approaches. In: Proceedings of the 4th ACM Conference on Recommender Systems. 2010, 199―206

[11] Weng J S, Lim E P, Jiang J, He Q. Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. 2010, 261―270

[12] Chen K L, Chen T Q, Zheng G Q, Jin O, Yao E P, Yu Y. Collaborative personalized tweet recommendation. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2012, 661―670

[13] Kim Y H, Shim K S. Twilite: A recommendation system for twitter using a probabilistic model based on latent dirichlet allocation. Information Systems, 2014, 42: 59―77

[14] Phelan O, McCarthy K, Smyth B. Using twitter to recommend realtime topical news. In: Proceedings of the 3rd ACM Conference on Recommender Systems. 2009, 385―388

[15] Sun A R, Cheng J S, Zeng D J. A novel recommendation framework for micro-blogging based on information diffusion. In: Proceedings of the 19th Workshop on Information Technologies and Systems. 2009, 199―216

[16] Feng H, Qian X M. Mining user-contributed photos for personalized product recommendation. Neurocomputing, 2014, 1: 409―420

[17] Jiang M, Cui P, Wang F, Yang Q, Zhu W W, Yang S Q. Social recommendation across multiple relational domains. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012, 1422―1431

[18] Liu S Y, Wang S H, Zhu F D, Zhang J B, Krishnan R. Hydra: largescale social identity linkage via heterogeneous behavior modeling. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. 2014, 51―62

[19] Rae A, Sigurbjornsson B, Zwol R V. Improving tag recommendation using social networks. In: Proceedings of the 9th RIAO Conference on Adaptivity, Personalization and Fusion of Heterogeneous Information. 2010, 92―99

[20] Cui P, Wang F, Liu S W, Ou M D, Yang S Q, Sun L F. Who should share what?: item-level social influence prediction for users and posts ranking. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2011, 185―194

[21] Hong L J, Doumith A S, Davison B D. Co-factorization machines: modeling user interests and predicting individual decisions in twitter. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining. 2013, 557―566

[22] Liu Q, Chen E H, Xiong H, Ding C H Q, Chen J. Enhancing collaborative filtering by user interest expansion via personalized ranking. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2012, 42(1): 218―233

[23] Koren Y. Collaborative filtering with temporal dynamics. Communications of the ACM, 2010, 53(4): 89―97

[24] 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. 2008, 426―434

[25] Koren Y. Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009, 447―456

[26] Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer, 2009, 42(8): 30―37

[27] Hu Y F, Koren Y, Volinsky C. Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE International Conference on Data Mining. 2008, 263―272

[28] Chen T Q, Zhang W N, Lu Q X, Chen K L, Zheng Z, Yu Y. SVDFeature: a toolkit for feature-based collaborative filtering. Journal of Machine Learning Research, 2012, 13: 3619―3622

[29] Jiang M, Cui P, Liu R, Yang Q, Wang F, Zhu W W, Yang S Q. Social contextual recommendation. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012, 45―54

[30] Jiang M, Cui P, Wang F, Zhu W W, Yang S Q. Scalable recommendation with social contextual information. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(11): 2789―2802

[31] Salakhutdinov R, Mnih A. Probabilistic matrix factorization. In: Proceedings of the 21st Annual Conference on Neural Information Processing Systems. 2007, 1257―1264

[32] Feng H, Qian X M. Recommendation via user’s personality and social contextual. In: Proceedings of the 22nd ACM International Conference on Conference on Information and Knowledge Management. 2013, 1521―1524

[33] Qian X M, Feng H, Zhao G S, Mei T. Personalized recommendation combining user interest and social circle. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(7): 1763―1777

[34] Yang L, Sun T, Zhang M, and Mei Q Z. We know what @ you #tag: does the dual role affect hashtag adoption? In: Proceedings of the 21st International Conference on World Wide Web. 2012, 261―270

[35] Wang X L, Wei F R, Liu X H, Zhou M, Zhang M. Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. 2011, 1031―1040

[36] Romero D M, Meeder B, Kleinberg J. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th International Conference on World Wide Web. 2011, 695―704

[37] Tsur O, Rappoport A. What’s in a hashtag?: content based prediction of the spread of ideas in microblogging communities. In: Proceedings of the 5th ACM International Conference on Web Search and Data Mining. 2012, 643―652

[38] Livne A, Simmons M, Adar E, Adamic L. The party is over here: Structure and content in the 2010 election. In: Proceedings of the 5th International Conference on Weblogs and Social Media. 2011, 201―208

[39] Meng X F, Wei F R, Liu X H, Zhou M, Li S J, Wang H F. Entity-centric topic-oriented opinion summarization in twitter. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012, 379―387

[40] Qazvinian V, Rosengren E, Radev D R, Mei Q Z. Rumor has it: identifying misinformation in microblogs. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2011, 1589―1599

[41] Godin F, Slavkovikj V, Neve WD. Using topic models for twitter hashtag recommendation. In: Proceedings of the 22nd International Conference on World Wide Web Companion. 2013, 593―596

[42] Kywe S M, Hoang T A, Lim E P, Zhu F D. On recommending hashtags in twitter networks. In: Proceedings of the 4th International Conference on Social Informatics. 2012, 337―350

[43] Zangerle E, Gassler W, Specht G. Using tag recommendations to homogenize folksonomies in microblogging environments. In: Proceedings of the 3rd International Conference on Social Informatics. 2011, 113―126

[44] Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation. The Journal of Machine Learning Research, 2003, 3(3): 993―1022

[45] Kullback S, Leibler R. On information and sufficiency. Annals of Mathematical Statistics, 1951, 22(1): 79―86

[46] Lehmann J, Goncalves B, Ramasco J J, Cattuto C. Dynamical classes of collective attention in twitter. In: Proceedings of the 21st International Conference on World Wide Web. 2012, 251―260

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