Abstract
In microblogs, authors use hashtags to mark keywords or topics. These manually labeled tags can be used to benefit various live social media applications (e.g., microblog retrieval, classification). However, because only a small portion of microblogs contain hashtags, recommending hashtags for use in microblogs are a worthwhile exercise. In addition, human inference often relies on the intrinsic grouping of words into phrases. However, existing work uses only unigrams to model corpora. In this work, we propose a novel phrase-based topical translation model to address this problem. We use the bag-of-phrases model to better capture the underlying topics of posted microblogs. We regard the phrases and hashtags in a microblog as two different languages that are talking about the same thing. Thus, the hashtag recommendation task can be viewed as a translation process from phrases to hashtags. To handle the topical information of microblogs, the proposed model regards translation probability as being topic specific. We test the methods on data collected from realworld microblogging services. The results demonstrate that the proposed method outperforms state-of-the-art methods that use the unigram model.
摘要
创新点
近几年微博标签推荐受到广泛关注, 当前用于标签推荐的模型主要基于词语级别, 然而一个短语往往表达的是一个含义, 认为短语中的每个词分别对齐到不同标签是不合理的。 因此本文提出了基于短语级别的标签推荐方法。
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References
Bermingham A, Smeaton A F. Classifying sentiment in microblogs: is brevity an advantage? In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management. New York: ACM, 2010. 1833–1836
Bollen J, Mao H, Zeng X. Twitter mood predicts the stock market. J Comput Sci, 2011, 2: 1–8
Pang B, Lee L. Opinion mining and sentiment analysis. Found Trends Inf Retr, 2008, 2: 1–135
Becker H, Naaman M, Gravano L. Learning similarity metrics for event identification in social media. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. New York: ACM, 2010. 291–300
Guy I, Avraham U, Carmel D, et al. Mining expertise and interests from social media. In: Proceedings of the 22nd International Conference on World Wide Web. New York: ACM, 2013. 515–526
Sakaki T, Okazaki M, Matsuo Y. Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web. New York: ACM, 2010. 851–860
Efron M. Hashtag retrieval in a microblogging environment. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2010. 787–788
Bandyopadhyay A, Mitra M, Majumder P. Query expansion for microblog retrieval. In: Proceedings of the 20th Text Retrieval Conference, TREC, 2011
Wang X, Wei F, Liu X, et al. 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. New York: ACM, 2011. 1031–1040
Bernhard D, Gurevych I. Combining lexical semantic resources with question & answer archives for translationbased answer finding. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. Stroudsburg: Association for Computational Linguistics, 2009. 2: 728–736
Liu Z Y, Liang C, Sun M S. Topical word trigger model for keyphrase extraction. In: Proceedings of the 24th International Conference on Computational Linguistics, Mumbai, 2012. 1715–1730
Zhao W X, Jiang J, Weng J, et al. Comparing twitter and traditional media using topic models. In: Proceedings of the 33rd European Conference on Advances in Information Retrieval. Berlin: Springer, 2011. 338–349
Diao Q M, Jiang J, Zhu F, et al. Finding bursty topics from microblogs. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2012. 536–544
Zhao W X, Jiang J, He J, et al. Topical keyphrase extraction from twitter. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Stroudsburg, 2011. 379–388
Ding Z Y, Qiu X, Zhang Q, et al. Learning topical translation model for microblog hashtag suggestion. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2013. 2078–2084
Ding Z, Zhang Q, Huang X. Automatic hashtag recommendation for microblogs using topic-specific translation model. In: Proceedings of the 24th International Conference on Computational Linguistics, Mumbai, 2012. 265
Chen K L, Chen T Q, Zheng G Q, et al. Collaborative personalized tweet recommendation. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2012. 661–670
Debnath S, Ganguly N, Mitra P. Feature weighting in content based recommendation system using social network analysis. In: Proceedings of the 17th International Conference onWorldWideWeb. New York: ACM, 2008. 1041–1042
Guy I, Zwerdling N, Ronen I, et al. Social media recommendation based on people and tags. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2010. 194–201
Konstas I, Stathopoulos V, Jose J M. On social networks and collaborative recommendation. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2009. 195–202
Pan Y, Cong F, Chen K, et al. Diffusion-aware personalized social update recommendation. In: Proceedings of the 7th ACM Conference on Recommender Systems. New York: ACM, 2013. 69–76
Ronen I, Guy I, Kravi E, et al. Recommending social media content to community owners. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2014. 243–252
Yan R, Lapata M, Li X. Tweet recommendation with graph co-ranking. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, Stroudsburg, 2012. 516–525
Chen W Y, Zhang D, Chang E Y. Combinational collaborative filtering for personalized community recommendation. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2008. 115–123
Lo S, Lin C. Wmr–a graph-based algorithm for friend recommendation. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, Hong Kong, 2006. 121–128
Ma H, King I, Lyu M R. Learning to recommend with social trust ensemble. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2009. 203–210
Moricz M, Dosbayev Y, Berlyant M. Pymk: friend recommendation at myspace. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM, 2010. 999–1002
Zhang W, Wang J, Feng W. Combining latent factor model with location features for event-based group recommendation. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2013. 910–918
Bu J J, Tan S L, Chen C, et al. Music recommendation by unified hypergraph: combining social media information and music content. In: Proceedings of the International Conference on Multimedia. New York: ACM, 2010. 391–400
Kaminskas M, Ricci F. Contextual music information retrieval and recommendation: state of the art and challenges. Comput Sci Rev, 2012, 6: 89–119
Schedl M, Schnitzer D. Hybrid retrieval approaches to geospatial music recommendation. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2013. 793–796
Li Q, Wang J, Chen Y P, et al. User comments for news recommendation in forum-based social media. Inform Sci, 2010, 180: 4929–4939
Shmueli E, Kagian A, Koren Y, et al. Care to comment? Recommendations for commenting on news stories. In: Proceedings of the 21st International Conference on World Wide Web. New York: ACM, 2012. 429–438
Vasuki V, Natarajan N, Lu Z, et al. Affiliation recommendation using auxiliary networks. In: Proceedings of the 4th ACM Conference on Recommender Systems. New York: ACM, 2010. 103–110
Heymann P, Ramage D, Garcia-Molina H. Social tag prediction. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2008. 531–538
Krestel R, Fankhauser P, Nejdl W. Latent dirichlet allocation for tag recommendation. In: Proceedings of the 3rd ACM Conference on Recommender Systems. New York: ACM, 2009. 61–68
Rendle S, Marinho L, Nanopoulos A, et al. Learning optimal ranking with tensor factorization for tag recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2009. 727–736
Song Y, Zhuang Z, Li H, et al. Real-time automatic tag recommendation. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2008. 515–522
Lu Y T, Yu S I, Chang T C, et al. A content-based method to enhance tag recommendation. In: Proceedings of the 21st International Jont Conference on Artifical Intelligence. San Francisco: Morgan Kaufmann Publishers Inc, 2009. 2064–2069
Tariq A, Karim A, Gomez F, et al. Exploiting topical perceptions over multi-lingual text for hashtag suggestion on twitter. In: Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference, St. Pete Beach, 2013. 474–479
Griffiths T L, Steyvers M. Finding scientific topics. Proc Natl Acad Sci, 2004, 101: 5228–5235
El-Kishky A, Song Y, Wang C, et al. Scalable topical phrase mining from text corpora. Proc VLDB Endowment, 2014, 8: 305–316
Brown P F, Pietra V J D, Pietra S A D, et al. The mathematics of statistical machine translation: parameter estimation. Comput Linguist, 1993, 19: 263–311
Och F J, Ney H. A systematic comparison of various statistical alignment models. Comput Linguist, 2003, 29: 19–51
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Gong, Y., Zhang, Q., Han, X. et al. Phrase-based hashtag recommendation for microblog posts. Sci. China Inf. Sci. 60, 012109 (2017). https://doi.org/10.1007/s11432-015-0900-x
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DOI: https://doi.org/10.1007/s11432-015-0900-x