APWeb-WAIM 2017: Web and Big Data pp 575-590 | Cite as

Incorporating User Preferences Across Multiple Topics into Collaborative Filtering for Personalized Merchant Recommendation

  • Yunfeng Chen
  • Lei Zhang
  • Xin Li
  • Yu Zong
  • Guiquan Liu
  • Enhong Chen
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10366)

Abstract

Merchant recommendation, namely recommending personalized merchants to a specific customer, has become increasingly important during the past few years especially with the prevalence of Location Based Social Networks (LBSNs). Although many existing methods attempt to address this task, most of them focus on applying the conventional recommendation algorithm (e.g. Collaborative Filtering) for merchant recommendation while ignoring harnessing the hidden information buried in the users’ reviews. In fact, the information of user real preferences on various topics hidden in the reviews is very useful for personalized merchant recommendation. To this end, in this paper, we propose a graphical model by incorporating user real preferences on various topics from user reviews into collaborative filtering technique for personalized merchant recommendation. Then, we develop an optimization algorithm based on a Gaussian model to train our merchant recommendation approach. Finally, we conduct extensive experiments on two real-world datasets to demonstrate the efficiency and effectiveness of our model. The experimental results clearly show that our proposed model outperforms the state-of-the-art benchmark approaches.

This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

This research was partially supported by grants from the National Natural Science Foundation of China (NSFC, Grant No. U1605251), the National Science Foundation for Distinguished Young Scholars of China (Grant No. 61325010), and the NSFC Major research program (Grant No. 91546103).

References

  1. 1.
    Bishop, C.M.: Probabilistic principal component analysis (1997)Google Scholar
  2. 2.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATHGoogle Scholar
  3. 3.
    Chaney, A.J., Blei, D.M., Eliassi-Rad, T.: A probabilistic model for using social networks in personalized item recommendation. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 43–50. ACM (2015)Google Scholar
  4. 4.
    Cheng, C., Yang, H., King, I., Lyu, M.R.: Fused matrix factorization with geographical and social influence in location-based social networks. AAAI 12, 17–23 (2012)Google Scholar
  5. 5.
    Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1082–1090. ACM (2011)Google Scholar
  6. 6.
    Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 39–46. ACM (2010)Google Scholar
  7. 7.
    Dueck, D., Frey, B., Dueck, D., Frey, B.J.: Probabilistic sparse matrix factorization. University of Toronto technical report PSI-2004-23 (2004)Google Scholar
  8. 8.
    Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends Hum.-Comput. Interact. 4(2), 81–173 (2011)CrossRefGoogle Scholar
  9. 9.
    Fan, M., Khademi, M.: Predicting a business star in yelp from its reviews text alone. arXiv preprint arXiv:1401.0864 (2014)
  10. 10.
    Gao, H., Tang, J., Hu, X., Liu, H.: Exploring temporal effects for location recommendation on location-based social networks. In Proceedings of the 7th ACM Conference on Recommender Systems, pp. 93–100. ACM (2013)Google Scholar
  11. 11.
    Huang, J., Rogers, S., Joo, E.: Improving restaurants by extracting subtopics from yelp reviews. In: iConference 2014 (Social Media Expo) (2014)Google Scholar
  12. 12.
    Karamshuk, D., Noulas, A., Scellato, S., Nicosia, V., Mascolo, C.: Geo-spotting: mining online location-based services for optimal retail store placement. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 793–801. ACM (2013)Google Scholar
  13. 13.
    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, pp. 426–434. ACM (2008)Google Scholar
  14. 14.
    Koren, Y., Bell, R., Volinsky, C., et al.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  15. 15.
    Li, H., Ge, Y., Zhu, H.: Point-of-interest recommendations: learning potential check-ins from friends. In: Proceedings of the 22th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2016)Google Scholar
  16. 16.
    Li, X., Xu, G., Chen, E., Li, L.: Learning user preferences across multiple aspects for merchant recommendation. In: 2015 IEEE International Conference on Data Mining (ICDM), pp. 865–870. IEEE (2015)Google Scholar
  17. 17.
    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, pp. 203–210. ACM (2009)Google Scholar
  18. 18.
    McAuley, J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In Proceedings of the 7th ACM Conference on Recommender Systems, pp. 165–172. ACM (2013)Google Scholar
  19. 19.
    Mukherjee, S., Basu, G., Joshi, S.: Incorporating author preference in sentiment rating prediction of reviews. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 47–48. ACM (2013)Google Scholar
  20. 20.
    Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-72079-9_10 CrossRefGoogle Scholar
  21. 21.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS 20, pp. 1–8 (2011)Google Scholar
  22. 22.
    Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 783–792. ACM (2010)Google Scholar
  23. 23.
    Weng, L.-T., Xu, Y., Li, Y., Nayak, R.: Exploiting item taxonomy for solving cold-start problem in recommendation making. In: 2008 20th IEEE International Conference on Tools with Artificial Intelligence, vol. 2, pp. 113–120. IEEE (2008)Google Scholar
  24. 24.
    Yuan, Q., Cong, G., Sun, A.: Graph-based point-of-interest recommendation with geographical and temporal influences. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 659–668. ACM (2014)Google Scholar
  25. 25.
    Zheng, V.W., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: a user-centered approach. AAAI 10, 236–241 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yunfeng Chen
    • 1
  • Lei Zhang
    • 2
  • Xin Li
    • 3
  • Yu Zong
    • 4
    • 5
  • Guiquan Liu
    • 1
  • Enhong Chen
    • 1
  1. 1.University of Science and Technology of ChinaHefeiChina
  2. 2.Anhui UniversityHefeiChina
  3. 3.IFlyTek ResearchHefeiChina
  4. 4.West Anhui UniversityLu’anChina
  5. 5.Texas A&M UniversityCollege StationUSA

Personalised recommendations