A Time and Sentiment Unification Model for Personalized Recommendation

  • Qinyong Wang
  • Hongzhi Yin
  • Hao Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10367)


With the rapid development of social media, personalized recommendation has become an essential means to help people discover attractive and interesting items. Intuitively, users buying items online are influenced not only by their preferences and public attentions, but also by the crowd sentiment (i.e., the word of mouth) to the items. Specifically, users are likely to refuse an item whose most reviews are negative from the crowd. Therefore, a good personalized recommendation model also needs to take crowd sentiment into account, which most current methods do not. In light of this, we propose TSUM, a model that jointly integrates time and crowd sentiment, for personalized recommendation in this paper. TSUM simultaneously models user-oriented topics related to user preferences, time-oriented topics relevant to temporal context, and crowd sentiment towards items. TSUM combines the influences of user preferences, temporal context and crowd sentiment to model user behavior in a unified way. Extensive experimental results on two large real world datasets show that our recommender system significantly outperforms the state-of-the-arts by making more effective personalized recommendations.


Temporal recommendation User behavior modeling Crowd sentiment 



The work described in this paper is supported by National Natural Science Foundation of China (61602453, 61672501, 61603373). It was also partially supported by ARC Discovery Early Career Researcher Award (DE160100308), ARC Discovery Project (DP170103954).


  1. 1.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. JMLR 3, 993–1022 (2003)zbMATHGoogle Scholar
  2. 2.
    Campos, P.G., Díez, F., Cantador, I.: Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Model. User Adapt. Interact. 24(1–2), 67–119 (2014)CrossRefGoogle Scholar
  3. 3.
    Chen, T., Zhang, W., Lu, Q., Chen, K., Zheng, Z., Yu, Y.: SVDFeature: a toolkit for feature-based collaborative filtering. JMLR 13(1), 3619–3622 (2012)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Diao, Q., Jiang, J., Zhu, F., Lim, E.P.: Finding bursty topics from microblogs. In: ACL, pp. 536–544. Association for Computational Linguistics (2012)Google Scholar
  5. 5.
    Gantner, Z., Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Mymedialite: a free recommender system library. In: RecSys, pp. 305–308. ACM (2011)Google Scholar
  6. 6.
    Hofmann, T.: Probabilistic latent semantic indexing. In: SIGIR, pp. 50–57. ACM (1999)Google Scholar
  7. 7.
    Hogg, T., Lerman, K.: Social dynamics of Digg. EPJ Data Sci. 1(1), 1–26 (2012)CrossRefGoogle Scholar
  8. 8.
    Iwata, T., Watanabe, S., Yamada, T., Ueda, N.: Topic tracking model for analyzing consumer purchase behavior. IJCAI 9, 1427–1432 (2009)Google Scholar
  9. 9.
    Jo, Y., Oh, A.H.: Aspect and sentiment unification model for online review analysis. In: WSDM, pp. 815–824. ACM (2011)Google Scholar
  10. 10.
    Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)CrossRefGoogle Scholar
  11. 11.
    Li, F., Wang, S., Liu, S., Zhang, M.: Suit: a supervised user-item based topic model for sentiment analysis. In: AAAI (2014)Google Scholar
  12. 12.
    Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: CIKM, pp. 375–384. ACM (2009)Google Scholar
  13. 13.
    Rosen-Zvi, M., Griffiths, T., Steyvers, M., Smyth, P.: The author-topic model for authors and documents. In: UAI, pp. 487–494. AUAI Press (2004)Google Scholar
  14. 14.
    Su, B., Ding, X.: Linear sequence discriminant analysis: a model-based dimensionality reduction method for vector sequences. In: ICCV, pp. 889–896 (2013)Google Scholar
  15. 15.
    Su, B., Ding, X., Liu, C., Wu, Y.: Heteroscedastic max-min distance analysis. In: CVPR, pp. 4539–4547 (2015)Google Scholar
  16. 16.
    Wang, H., Xu, F., Hu, X., Ohsawa, Y.: Ideagraph: a graph-based algorithm of mining latent information for human cognition. In: SMC, pp. 952–957. IEEE (2013)Google Scholar
  17. 17.
    Wang, H., Zhang, C., Wang, W., Hu, X., Xu, F.: Human-centric computational knowledge environment for complex or ill-structured problem solving. In: SMC, pp. 2940–2945. IEEE (2014)Google Scholar
  18. 18.
    Wang, H., Zhang, C., Yin, H., Wang, W., Zhang, J., Xu, F.: A unified framework for fine-grained opinion mining from online reviews. In: HICSS, pp. 1134–1143. IEEE (2016)Google Scholar
  19. 19.
    Wang, W., Yin, H., Sadiq, S., Chen, L., Xie, M., Zhou, X.: Spore: a sequential personalized spatial item recommender system. In: ICDE, pp. 954–965. IEEE (2016)Google Scholar
  20. 20.
    Wang, X., McCallum, A.: Topics over time: a non-Markov continuous-time model of topical trends. In: SIGKDD, pp. 424–433. ACM (2006)Google Scholar
  21. 21.
    Xie, M., Yin, H., Wang, H., Xu, F., Chen, W., Wang, S.: Learning graph-based POI embedding for location-based recommendation. In: CIKM, pp. 15–24. ACM (2016)Google Scholar
  22. 22.
    Yin, H., Cui, B., Chen, L., Hu, Z., Zhou, X.: Dynamic user modeling in social media systems. TOIS 33(3), 10 (2015)CrossRefGoogle Scholar
  23. 23.
    Yin, H., Cui, B., Lu, H., Huang, Y., Yao, J.: A unified model for stable and temporal topic detection from social media data. In: ICDE, pp. 661–672. IEEE (2013)Google Scholar
  24. 24.
    Yin, H., Cui, B., Zhou, X., Wang, W., Huang, Z., Sadiq, S.: Joint modeling of user check-in behaviors for real-time point-of-interest recommendation. TOIS 35(2), 11 (2016)CrossRefGoogle Scholar
  25. 25.
    Yin, H., Zhou, X., Cui, B., Wang, H., Zheng, K., Nguyen, Q.V.H.: Adapting to user interest drift for POI recommendation. ICDE 28(10), 2566–2581 (2016)Google Scholar
  26. 26.
    Zhao, T., Li, C., Ding, Q., Li, L.: User-sentiment topic model: refining user’s topics with sentiment information. In: SIGKDD Workshop on Mining Data Semantics, p. 10. ACM (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Software, Chinese Academy of SciencesUniversity of Chinese Academy of SciencesBeijingChina
  2. 2.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia

Personalised recommendations