Abstract
Personalized recommender system has become an essential means to help people discover attractive and interesting items. We find that to buy an item, a user is influenced not only by her intrinsic interests and temporal contexts, but also by the crowd sentiment to this item. Users tend to refuse to accept the recommended items whose most reviews are negative. In light of this, we propose a temporal-sentiment-aware user behavior model (TSAUB) to learn personal interests, temporal contexts (i.e., temporal preferences of the public) and crowd sentiment from user review data. Based on the learnt knowledge from TSAUB, we design a temporal-sentiment-aware recommender system. To improve the training efficiency of TSAUB, we develop a distributed learning algorithm for model parameter estimation using the Spark framework. Extensive experiments have been performed on four Amazon datasets, and the results show that our recommender system significantly outperforms the state-of-the-arts by making more effective and efficient recommendations.
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
Notes
- 1.
We use “behaviors” to refer to a broad range of user actions such as purchases, clicks and writing reviews.
References
Bi, B., Tian, Y., Sismanis, Y., Balmin, A., Cho, J.: Scalable topic-specific influence analysis on microblogs. In: WSDM, pp. 513–522 (2014)
Chen, W.Y., Chu, J.C., Luan, J., Bai, H., Wang, Y., Chang, E.Y.: Collaborative filtering for orkut communities: discovery of user latent behavior. In: WWW, pp. 681–690 (2009)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: RecSys, pp. 39–46 (2010)
García-Cumbreras, M.Á., Montejo-Ráez, A., Díaz-Galiano, M.C.: Pessimists and optimists: improving collaborative filtering through sentiment analysis. Expert Syst. Appl. 40(17), 6758–6765 (2013)
Herr, P.M., Kardes, F.R., Kim, J.: Effects of word-of-mouth and product-attribute information on persuasion: an accessibility-diagnosticity perspective. J. Consum. Res. 17(4), 454–462 (1991)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD, pp. 426–434 (2008)
Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)
Li, L., Chu, W., Langford, J., Schapire, R.E.: A contextual-bandit approach to personalized news article recommendation. In: WWW, pp. 661–670 (2010)
McAuley, J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: RecSys, pp. 165–172 (2013)
Pappas, N., Popescu-Belis, A.: Sentiment analysis of user comments for one-class collaborative filtering over ted talks. In: SIGIR, pp. 773–776 (2013)
Pero, Š., Horváth, T.: Opinion-driven matrix factorization for rating prediction. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 1–13. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38844-6_1
Rendle, S.: Factorization machines. In: ICDM, pp. 995–1000 (2010)
Rosen-Zvi, M., Griffiths, T., Steyvers, M., Smyth, P.: The author-topic model for authors and documents. In: UAI, pp. 487–494 (2004)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295 (2001)
Singh, V.K., Mukherjee, M., Mehta, G.K.: Combining collaborative filtering and sentiment classification for improved movie recommendations. In: Sombattheera, C., Agarwal, A., Udgata, S.K., Lavangnananda, K. (eds.) MIWAI 2011. LNCS (LNAI), vol. 7080, pp. 38–50. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25725-4_4
Stoyanovich, J., Amer-Yahia, S., Marlow, C., Yu, C.: Leveraging tagging to model user interests in del. icio. us. In: AAAI, pp. 104–109 (2008)
Tang, J., Wu, S., Sun, J., Su, H.: Cross-domain collaboration recommendation. In: KDD, pp. 1285–1293 (2012)
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 (2016)
Xiong, L., Chen, X., Huang, T.K., Schneider, J.G., Carbonell, J.G.: Temporal collaborative filtering with bayesian probabilistic tensor factorization. In: SDM, pp. 211–222 (2010)
Xu, Z., Zhang, Y., Wu, Y., Yang, Q.: Modeling user posting behavior on social media. In: SIGIR, pp. 545–554 (2012)
Yin, H., Cui, B., Chen, L., Hu, Z., Zhang, C.: Modeling location-based user rating profiles for personalized recommendation. TKDE 9(3), 19 (2015)
Yin, H., Cui, B., Chen, L., Hu, Z., Zhou, X.: Dynamic user modeling in social media systems. TOIS 33(3), 10 (2015)
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 (2013)
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)
Yin, H., Sun, Y., Cui, B., Hu, Z., Chen, L.: LCARS: a location-content-aware recommender system. In: KDD, pp. 221–229 (2013)
Yin, H., Wang, W., Wang, H., Chen, L., Zhou, X.: Spatial-aware hierarchical collaborative deep learning for poi recommendation. TKDE 29(11), 2537–2551 (2017)
Yin, H., Zhou, X., Cui, B., Wang, H., Zheng, K., Nguyen, Q.V.H.: Adapting to user interest drift for poi recommendation. TKDE 28(10), 2566–2581 (2016)
Yin, H., Zhou, X., Shao, Y., Wang, H., Sadiq, S.: Joint modeling of user check-in behaviors for point-of-interest recommendation. In: CIKM, pp. 1631–1640 (2015)
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M.J., Shenker, S., Stoica, I.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: NSDI, p. 2 (2012)
Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: HotCloud, p. 10 (2010)
Zhang, Y., Lai, G., Zhang, M., Zhang, Y., Liu, Y., Ma, S.: Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In: SIGIR, pp. 83–92 (2014)
Acknolwledgement
This work was supported by ARC Discovery Early Career Researcher Award (Grant No. DE160100308), ARC Discovery Project (Grant No. DP170103954) and New Staff Research Grant of The University of Queensland (Grant No.613134).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Wang, Q., Yin, H., Wang, H., Huang, Z. (2018). TSAUB: A Temporal-Sentiment-Aware User Behavior Model for Personalized Recommendation. In: Wang, J., Cong, G., Chen, J., Qi, J. (eds) Databases Theory and Applications. ADC 2018. Lecture Notes in Computer Science(), vol 10837. Springer, Cham. https://doi.org/10.1007/978-3-319-92013-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-92013-9_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92012-2
Online ISBN: 978-3-319-92013-9
eBook Packages: Computer ScienceComputer Science (R0)