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Journal of Computer Science and Technology

, Volume 33, Issue 4, pp 727–738 | Cite as

A Generative Model Approach for Geo-Social Group Recommendation

  • Peng-Peng Zhao
  • Hai-Feng Zhu
  • Yanchi Liu
  • Zi-Ting Zhou
  • Zhi-Xu Li
  • Jia-Jie Xu
  • Lei Zhao
  • Victor S. Sheng
Regular Paper
  • 26 Downloads

Abstract

With the development and prevalence of online social networks, there is an obvious tendency that people are willing to attend and share group activities with friends or acquaintances. This motivates the study on group recommendation, which aims to meet the needs of a group of users, instead of only individual users. However, how to aggregate different preferences of different group members is still a challenging problem: 1) the choice of a member in a group is influenced by various factors, e.g., personal preference, group topic, and social relationship; 2) users have different influences when in different groups. In this paper, we propose a generative geo-social group recommendation model (GSGR) to recommend points of interest (POIs) for groups. Specifically, GSGR well models the personal preference impacted by geographical information, group topics, and social influence for recommendation. Moreover, when making recommendations, GSGR aggregates the preferences of group members with different weights to estimate the preference score of a group to a POI. Experimental results on two datasets show that GSGR is effective in group recommendation and outperforms the state-of-the-art methods.

Keywords

group recommendation topic model social network 

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References

  1. [1]
    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. ACM Trans. Inf. Syst., 2016, 35(2): 11:1-11:44.Google Scholar
  2. [2]
    Chen E, Xu T, Tian J, Yang Y. Personalized recommendation via mobile context-aware mining. Communication of China Computer Federation, 2013(3): 18-24. (in Chinese)Google Scholar
  3. [3]
    Yin H, Wang W, Wang H, Chen L, Zhou X. Spatial-aware hierarchical collaborative deep learning for POI recommendation. IEEE Trans. Knowl. Data Eng., 2017, 29(11): 2537-2551.CrossRefGoogle Scholar
  4. [4]
    Ayala-Gómez F, Daróczy B Z, Mathioudakis M, Benczúr A, Gionis A. Where could we go? Recommendations for groups in location-based social networks. In Proc. Web Science Conference, June 2017, pp.93-102.Google Scholar
  5. [5]
    Liu X, Tian Y, Ye M, Lee W C. Exploring personal impact for group recommendation. In Proc. the 21st ACM Int. Conf. Information and Knowledge Management, October 2012, pp.674-683.Google Scholar
  6. [6]
    Ye M, Liu X, Lee WC. Exploring social influence for recommendation: A generative model approach. In Proc. the 35th Int. ACM SIGIR Conference on Research and Development in Information Retrieval, August 2012, pp.671-680.Google Scholar
  7. [7]
    Rakesh V, Lee WC, Reddy C K. Probabilistic group recommendation model for crowdfunding domains. In Proc. the 9th ACM Int. Conf. Web Search and Data Mining, February 2016, pp.257–266.Google Scholar
  8. [8]
    Yuan Q, Cong G, Lin C Y. COM: A generative model for group recommendation. In Proc. the 20th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, August 2014, pp.163-172.Google Scholar
  9. [9]
    Tang J, Wang S, Hu X, Yin D, Bi Y, Chang Y, Liu H. Recommendation with social dimensions. In Proc. the 30th AAAI Int. Conf. Artificial Intelligence, February 2016, pp.251-257.Google Scholar
  10. [10]
    Yuan Q, Cong G, Ma Z, Sun A, Thalmann N M. Time-aware point-of-interest recommendation. In Proc. the 36th Int. ACM SIGIR Conf. Research and Development in Information Retrieval, August 2013, pp.363-372.Google Scholar
  11. [11]
    Yin H, Cui B. Spatio-Temporal Recommendation in Social Media (1st edition). Springer, 2016.Google Scholar
  12. [12]
    Liu X, He Q, Tian Y, Lee WC, McPherson J, Han J. Event-based social networks: Linking the online and offline social worlds. In Proc. the 18th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, August 2012, pp.1032-1040.Google Scholar
  13. [13]
    Masthoff J. Group modeling: Selecting a sequence of television items to suit a group of viewers. User Model. User-Adapt. Interact., 2004, 14(1): 37-85.CrossRefGoogle Scholar
  14. [14]
    O’connor M, Cosley D, Konstan J A, Riedl J. PolyLens: A recommender system for groups of users. In Proc. the 7th European Conf. Computer Supported Cooperative Work, September 2001, pp.199-218.Google Scholar
  15. [15]
    Amer-Yahia S, Roy S B, Chawlat A, Das G, Yu C. Group recommendation: Semantics and efficiency. Proc. the VLDB Endowment, 2009, 2(1): 754-765.CrossRefGoogle Scholar
  16. [16]
    Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. IEEE Computer, 2009, 42(8): 30-37.CrossRefGoogle Scholar
  17. [17]
    Yin H, Sun Y, Cui B, Hu Z, Chen L. LCARS: A location-content-aware recommender system. In Proc. the 19th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, August 2013, pp.221-229.Google Scholar
  18. [18]
    Zhang J D, Chow C Y. GeoSoCa: Exploiting geographical, social and categorical correlations for point-of-interest recommendations. In Proc. the 38th Int. ACM SIGIR Conf. Research and Development in Information Retrieval, August 2015, pp.443-452.Google Scholar
  19. [19]
    Yin H, Zhou X, Cui B, Wang H, Zheng K, Nguyen Q V H. Adapting to user interest drift for POI recommendation. IEEE Trans. Knowl. Data Eng., 2016, 28(10): 2566-2581.CrossRefGoogle Scholar
  20. [20]
    Xie M, Yin H, Wang H, Xu F, Chen W, Wang S. Learning graph-based POI embedding for location-based recommendation. In Proc. the 25th ACM Int. Conf. Information and Knowledge Management, October 2016, pp.15-24.Google Scholar
  21. [21]
    Yin H, Hu Z, Zhou X, Wang H, Zheng K, Nguyen Q V H, Sadiq S. Discovering interpretable geo-social communities for user behavior prediction. In Proc. the 32nd IEEE Int. Conf. the Data Engineering, May 2016, pp.942-953.Google Scholar
  22. [22]
    Zhao P, Xu X, Liu Y, Zhou Z, Zheng K, Sheng V S, Xiong H. Exploiting hierarchical structures for POI recommendation. In Proc. IEEE Int. Conf. Data Mining, November 2017, pp.655-664.Google Scholar
  23. [23]
    Zhao P, Xu X, Liu Y, Sheng V S, Zheng K, Xiong H. Photo2Trip: Exploiting visual contents in geo-tagged photos for personalized tour recommendation. In Proc. ACM on Multimedia Conference, October 2017, pp.916-924.Google Scholar
  24. [24]
    Pizzutilo S, de Carolis B, Cozzolongo G, Ambruoso F. Group modeling in a public space: Methods, techniques, experiences. In Proc. the 5th World Scientific and Engineering Academy and Society Int. Conf. Applied Informatics and Communications, Sept. 2005, pp.175-180.Google Scholar
  25. [25]
    McCarthy K, Salamó M, Coyle L, McGinty L, Smyth B, Nixon P. CATS: A synchronous approach to collaborative group recommendation. In Proc. Florida Artificial Intelligence Research Society Conference, May 2006, pp.86-91.Google Scholar
  26. [26]
    Crossen A, Budzik J, Hammond K J. Flytrap: Intelligent group music recommendation. In Proc. the 7th ACM Int. Conf. Intelligent User Interfaces, January 2002, pp.184-185.Google Scholar
  27. [27]
    Yu Z, Zhou X, Hao Y, Gu J. TV program recommendation for multiple viewers based on user profile merging. User Model. User-Adapt. Interact., 2006, 16(1): 63-82.CrossRefGoogle Scholar
  28. [28]
    McCarthy J F, Anagnost T D. MusicFX: An arbiter of group preferences for computer supported collaborative workouts. In Proc. ACM Conf. Computer Supported Cooperative Work, Nov. 1998, pp.363-372.Google Scholar
  29. [29]
    Baltrunas L, Makcinskas T, Ricci F. Group recommendations with rank aggregation and collaborative filtering. In Proc. the 4th ACM Conf. Recommender Systems, September 2010, pp.119-126.Google Scholar
  30. [30]
    Seko S, Yagi T, Motegi M, Muto S. Group recommendation using feature space representing behavioral tendency and power balance among members. In Proc. the 5th ACM Conf. Recommender Systems, October 2011, pp.101-108.Google Scholar

Copyright information

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

Authors and Affiliations

  • Peng-Peng Zhao
    • 1
  • Hai-Feng Zhu
    • 1
  • Yanchi Liu
    • 2
  • Zi-Ting Zhou
    • 1
  • Zhi-Xu Li
    • 1
  • Jia-Jie Xu
    • 1
  • Lei Zhao
    • 1
  • Victor S. Sheng
    • 3
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Department of Management Science and Information SystemsRutgers UniversityPiscatawayU.S.A.
  3. 3.Department of Computer ScienceUniversity of Central ArkansasConwayU.S.A.

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