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Journal of Computer Science and Technology

, Volume 33, Issue 4, pp 711–726 | Cite as

Hashtag Recommendation Based on Multi-Features of Microblogs

  • Fei-Fei Kou
  • Jun-Ping Du
  • Cong-Xian Yang
  • Yan-Song Shi
  • Wan-Qiu Cui
  • Mei-Yu Liang
  • Yue Geng
Regular Paper
  • 4 Downloads

Abstract

Hashtag recommendation for microblogs is a very hot research topic that is useful to many applications involving microblogs. However, since short text in microblogs and low utilization rate of hashtags will lead to the data sparsity problem, it is difficult for typical hashtag recommendation methods to achieve accurate recommendation. In light of this, we propose HRMF, a hashtag recommendation method based on multi-features of microblogs in this article. First, our HRMF expands short text into long text, and then it simultaneously models multi-features (i.e., user, hashtag, text) of microblogs by designing a new topic model. To further alleviate the data sparsity problem, HRMF exploits hashtags of both similar users and similar microblogs as the candidate hashtags. In particular, to find similar users, HRMF combines the designed topic model with typical user-based collaborative filtering method. Finally, we realize hashtag recommendation by calculating the recommended score of each hashtag based on the generated topical representations of multi-features. Experimental results on a real-world dataset crawled from Sina Weibo demonstrate the effectiveness of our HRMF for hashtag recommendation.

Keywords

hashtag recommendation topic model collaborative filtering method microblog 

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References

  1. [1]
    Bai T, Dou H J, Zhao W X, Yang D Y, Wen J R. An experimental study of text representation methods for cross-site purchase preference prediction using the social text data. Journal of Computer Science and Technology, 2017, 32(4): 828-842.CrossRefGoogle Scholar
  2. [2]
    ChenW, Yin H, Wang W, Zhao L, Hua W, Zhou X. Exploiting spatio-temporal user behaviors for user linkage. In Proc. the 2017 ACM Conference on Information and Knowledge Management (CIKM), November 2017, pp.517-526.Google Scholar
  3. [3]
    Wang W, Yin H, Sadiq S, Chen L, Xie M, Zhou X. STSAGE: A spatial-temporal sparse additive generative model for spatial item recommendation. ACM Transactions on Intelligent Systems and Technology (TIST), 2017, 8(3): Article No. 48.Google Scholar
  4. [4]
    Deng L, Jia Y, Zhou B, Huang J, Han Y. User interest mining via tags and bidirectional interactions on Sina Weibo. In Proc. the 26th International Conference on World Wide Web, April 2017, pp.1-22.Google Scholar
  5. [5]
    Chen H, Yin H, Li X, Wang M, Chen W, Chen T. People opinion topic model: Opinion based user clustering in social networks. In Proc. the 26th International Conference on World Wide Web Companion, April 2017, pp.1353-1359.Google Scholar
  6. [6]
    Hu F, Li L, Zhang Z L, Wang J Y, Xu X F. Emphasizing essential words for sentiment classification based on recurrent neural networks. Journal of Computer Science and Technology, 2017, 32(4): 785-795.CrossRefGoogle Scholar
  7. [7]
    Wang Y, Liu J, Huang Y, Feng X. Using hashtag graph-based topic model to connect semantically-related words without co-occurrence in microblogs. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(7): 1919-1933.CrossRefGoogle Scholar
  8. [8]
    Bansal P, Jain S, Varma V. Towards semantic retrieval of hashtags in microblogs. In Proc. the 24th International Conference on World Wide Web (WWW), May 2015, pp.7-8.Google Scholar
  9. [9]
    Gong Y, Zhang Q, Huang X. Hashtag recommendation for multimodal microblog posts. Neurocomputing, 2018, 272: 170-177.CrossRefGoogle Scholar
  10. [10]
    Ding Z, Qiu X, Zhang Q, Huang X. Learning topical translation model for microblog hashtag suggestion. In Proc. the 2013 Joint Conference on Artificial Intelligence, July 2013, pp.2078-2084.Google Scholar
  11. [11]
    Godin F, Slavkovikj V, de Neve W, Schrauwen B, van de Walle R. Using topic models for twitter hashtag recommendation. In Proc. the 2013 International World Wide Web Conferences Steering Committee, April 2013, pp.593-596.Google Scholar
  12. [12]
    Zhao F, Zhu Y, Jin H, Yang L T. A personalized hashtag recommendation approach using LDA-based topic model in microblog environment. Future Generation Computer Systems, 2016, 65(C): 196-206.CrossRefGoogle Scholar
  13. [13]
    Li J, Xu H, He X, Deng J, Sun X. Tweet modeling with LSTM recurrent neural networks for hashtag recommendation. In Proc. the International Joint Conference on Neural Networks (IJCNN), July 2016, pp.1570-1577.Google Scholar
  14. [14]
    Sedhai S, Sun A. Hashtag recommendation for hyperlinked tweets. In Proc. the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, July 2014, pp.831-834.Google Scholar
  15. [15]
    Kywe S, Hoang T A, Lim E P, Zhu F. On recommending hashtags in twitter networks. Social Informatics, 2012: 337-350.Google Scholar
  16. [16]
    Wang Y, Qu J, Liu J, Chen J, Huang Y. What to tag your microblog: Hashtag recommendation based on topic analysis and collaborative filtering. In Proc. the Asia-Pacific Web Conference (APWeb), September 2014, pp.610-618.Google Scholar
  17. [17]
    Mikolov T, Sutskever I, Chen K, Corrado G S, Dean J. Distributed representations of words and phrases and their compositionality. In Proc. the 2013 Advances in Neural Information Processing Systems (NIPS), December 2013, pp.3111-3119.Google Scholar
  18. [18]
    Arora S, Liang Y, Ma T. A simple but tough-to-beat baseline for sentence embeddings. In Proc. the 2017 International Conference on Learning Representations, April 2017.Google Scholar
  19. [19]
    Li Q, Shah S, Nourbakhsh A, Liu X, Fang R. Hashtag recommendation based on topic enhanced embedding, tweet entity data and learning to rank. In Proc. the 2016 ACM International Conference on Information and Knowledge Management (CIKM), October 2016, pp.2085-2088.Google Scholar
  20. [20]
    Li J, Xu H. Suggest what to tag: Recommending more precise hashtags based on users’ dynamic interests and streaming tweet content. Knowledge-Based Systems, 2016, 106: 196-205.CrossRefGoogle Scholar
  21. [21]
    Zhou X, Chen L, Zhang Y, Qin D, Cao L, Huang G, Wang C. Enhancing online video recommendation using social user interactions. VLDB Journal, 2017(1): 1-20.Google Scholar
  22. [22]
    She J, Chen L. TOMOHA: Topic model-based hashtag recommendation on twitter. In Proc. the 23rd International Conference on World Wide Web (WWW), April 2014, pp.371-372.Google Scholar
  23. [23]
    Song S, Meng Y, Zheng Z. Recommending hashtags to forthcoming tweets in microblogging. In Proc. the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), January 2016.Google Scholar
  24. [24]
    Li T, Wu Y, Zhang Y. Twitter hash tag prediction algorithm. In Proc. the International Conference on Internet Computing (ICOMP), July 2011.Google Scholar
  25. [25]
    Tomar A, Godin F, Vandersmissen B, de Neve W, van de Walle R. Towards Twitter hashtag recommendation using distributed word representations and a deep feed forward neural network. In Proc. the Advances in Computing, Communications and Informatics (ICACCI), September 2014, pp.362-368.Google Scholar
  26. [26]
    Zhang Y, Xiao Y, Hwang S W, Wang H, Wang X S, Wang W. Entity suggestion with conceptual explanation. In Proc. the 26th International Joint Conference on Artificial Intelligence (IJCAI), August 2017, pp.4244-4250.Google Scholar
  27. [27]
    Tong Y, Chen L, Zhou Z, Jagadish H V, Shou L, Lv W. SLADE: A smart large-scale task decomposer in crowd-sourcing. IEEE Transactions on Knowledge and Data Engineering, DOI:  https://doi.org/10.1109/TKDE.2018.2797962.
  28. [28]
    Cao C C, Tong Y, Chen L, Jagadish H V. WiseMarket: A new paradigm for managing wisdom of online social users. In Proc. the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), August 2013, pp.455-463.Google Scholar
  29. [29]
    She J, Tong Y, Chen L, Cao C C. Conflict-aware event-participant arrangement and its variant for online setting. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(9): 2281-2295.CrossRefGoogle Scholar
  30. [30]
    Tong Y, She Y, Meng R. Bottleneck-aware arrangement over event-based social networks: The Max-min approach. World Wide Web Journal, 2016, 19(6): 1151-1177.CrossRefGoogle Scholar
  31. [31]
    Tong Y, She J, Chen L. Towards better understanding of app functions. Journal of Computer Science and Technology, 2015, 30(5): 1130-1140.CrossRefGoogle Scholar
  32. [32]
    Tong Y, Cao C C, Chen L. TCS: Efficient topic discovery over crowd-oriented service data. In Proc. the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), August 2014, pp.861-870.Google Scholar
  33. [33]
    Jiang D, Tong Y, Song Y. Cross-lingual topic discovery from multilingual search engine query log. ACM Transactions on Information Systems, 2016, 35(2): Article No. 9.Google Scholar
  34. [34]
    Bicalho P, Pita M, Pedrosa G, Lacerda A, Pappa G L. A general framework to expand short text for topic modeling. Information Sciences, 2017, 393: 66-81.CrossRefGoogle Scholar
  35. [35]
    Zhao Y, Liang S, Ren Z, Ma J, Yilmaz E, de Rijke M. Explainable user clustering in short text streams. In Proc. the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2016, pp.155-164.Google Scholar
  36. [36]
    Yan X, Guo J, Lan Y, Cheng X. A biterm topic model for short texts. In Proc. International Conference on World Wide Web (WWW), May 2013, pp.1445-1456.Google Scholar
  37. [37]
    Chen T, SalahEldeen H M, He X, Kan M Y, Lu D. VELDA: Relating an image tweet’s text and images. In Proc. the 29th AAAI Conference on Artificial Intelligence, January 2015, pp.30-36.Google Scholar
  38. [38]
    Newman D, Lau J H, Grieser K, Baldwin T. Automatic evaluation of topic coherence. In Proc. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, June 2010, pp.100-108.Google Scholar
  39. [39]
    Li C, Duan Y, Wang H, Zhang Z, Sun A, Ma Z. Enhancing topic modeling for short texts with auxiliary word embeddings. ACM Transactions on Information Systems, 2017, 36(2): Article No. 11.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Fei-Fei Kou
    • 1
  • Jun-Ping Du
    • 1
  • Cong-Xian Yang
    • 1
  • Yan-Song Shi
    • 1
  • Wan-Qiu Cui
    • 1
  • Mei-Yu Liang
    • 1
  • Yue Geng
    • 1
  1. 1.Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science Beijing University of Posts and TelecommunicationsBeijingChina

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