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JACIII Vol.20 No.6 pp. 882-892
doi: 10.20965/jaciii.2016.p0882
(2016)

Paper:

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Predicting POI Visits in a Heterogeneous Location-Based Social Network

Zih-Syuan Wang, Jing-Fu Juang, and Wei-Guang Teng

Department of Engineering Science, National Cheng Kung University
No.1, University Road, Tainan City 701, Taiwan

Received:
April 1, 2016
Accepted:
July 1, 2016
Published:
November 20, 2016
Creative Commons License
Keywords:
link prediction, meta-path, POI recommendation, social network analysis
Abstract
A point of interest (POI) is a specific location that people may find useful or interesting, such as restaurants, stores, attractions, and hotels. With the recent proliferation of location-based social networks (LBSN), numerous users gather to interact and share information on various POIs. POI recommendations have become a crucial issue because it not only helps users to learn about new places but also gives LBSN providers chances to post POI advertisements. As we utilize a heterogeneous information network to represent an LBSN in this work, POI recommendations are remodeled as a link prediction problem, which is significant in the field of social network analysis. Moreover, we propose to utilize the meta-path-based approach to extract implicit but potentially useful relationships between a user and a POI. Then, the extracted topological features are used to construct a prediction model with appropriate data classification techniques. In our experiments, the Yelp dataset is utilized as our testbed for performance evaluation purposes. The results show that our prediction model is of good prediction quality in practical applications.
Cite this article as:
Z. Wang, J. Juang, and W. Teng, “Predicting POI Visits in a Heterogeneous Location-Based Social Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.6, pp. 882-892, 2016.
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References
  1. [1] C.-H. Chen and Y. Takama, “Identification of Season-Dependent Sightseeing Spots Based on Metadata-Derived Features and Image Processing,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.18, No.3, pp. 353-360, 2014.
  2. [2] B. Karimi and M. H. Yektaei, “Location Recommendation Based on Location-Based Social Networks for Entertainment Services,” Advances in Computer Science: an Int. J., Vol.4, 1:13, pp. 55-64, 2015.
  3. [3] A. Tiroshi, S. Berkovsky, M. A. Kaafar, D. Vallet, T. Chen, and T. Kuflik, “Improving Business Rating Predictions Using Graph Based Features,” Proc. of the Int. Conf. on Intelligent User Interfaces, pp. 17-26, 2014.
  4. [4] Y. Yu and X. Chen, “A Survey of Point-of-Interest Recommendation in Location-Based Social Networks,” Proc. of 2015 AAAI Workshop on Trajectory-Based Behaviour Analytics, pp. 53-60, 2015.
  5. [5] J. Zhang, X. Kong, and P. S. Yu, “Transferring Heterogeneous Links Across Location-Based Social Networks,” Proc. of the 7th ACM Int. Conf. on Web Search and Data Mining, pp. 303-312, 2014.
  6. [6] F. Wang, G. Wang, and P. S. Yu, “Why Checkins: Exploring User Motivation on Location Based Social Networks,” Proc. of the IEEE Int. Conf. on Data Mining Workshops, pp. 27-34, December 2014.
  7. [7] L. Hu, A. Sun, and Y. Liu, “Your Neighbors Affect Your Ratings: On Geographical Neighborhood Influence to Rating Prediction,” Proc. of the 37th ACM SIGIR Int. Conf. on Research & Development in Information Retrieval, pp. 345-354, 2014.
  8. [8] S. Hattori and Y. Takama, “Recommender System Employing Personal-Value-Based User Model,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.18, No.2, pp. 157-165, 2014.
  9. [9] M. Ye, P. Yin, W.-C. Lee, and D.-L. Lee, “Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation,” Proc. of the 34th ACM SIGIR Int. Conf. on Research and Development in Information Retrieval, pp. 325-334, 2011.
  10. [10] Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. Magnenat-Thalmann, “Time-aware Point-of-interest Recommendation,” Proc. of the 36th ACM SIGIR Int. Conf. on Research and Development in Information Retrieval, pp. 363-372, 2013.
  11. [11] Y. Sun, R. Barber, M. Gupta, C. C. Aggarwal, and J. Han, “Co-author Relationship Prediction in Heterogeneous Bibliographic Networks,” Proceeding of the 2011 Int. Conf. on Advances in Social Networks Analysis and Mining, pp. 121-128, 2011.
  12. [12] Y. Sun and J. Han, “Mining Heterogeneous Information Networks: A Structural Analysis Approach,” ACM SIGKDD Explorations Newsletter, Vol.14, No.2, pp. 20-28, 2012.
  13. [13] Z.-S. Wang, J.-F. Juang, and W.-G. Teng, “Predicting POI Visits with a Heterogeneous Information Network,” Proc. of the 2015 Conf. on Technologies and Applications of Artificial Intelligence, pp. 388-395, Tainan, Taiwan, November 20-22, 2015.
  14. [14] Y. Lyu, C.-Y. Chow, R. Wang, and V. C. S. Lee, “Using Multi-Criteria Decision Making for Personalized Point-of-Interest Recommendations,” Proc. of the 22nd ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems, pp. 461-464, 2014.
  15. [15] P. Kosmida, C. Remoundou, K. Demestichas, I. Loumiotis, E. Adamopoulou, and M. Theologou, “A Location Recommender System for Location-Based Social Networks,” Proc. of the Int. Conf. on Mathematics and Computers in Sciences and in Industry, pp. 277-280, September 2014.
  16. [16] X. Long and J. Joshi, “A HITS-based POI Recommendation Algorithm for Location-Based Social Networks,” Proc. of ACM Int. Conf. on Advances in Social Networks Analysis and Mining, pp. 642-647, 2013.
  17. [17] J. J-C. Ying, E. H-C. Lu, W.-N. Kuo, and V. S. Tseng, “Urban Point-of-Interest Recommendation by Mining User Check-in Behaviors,” Proc. of the ACM SIGKDD Int. Workshop on Urban Computing, pp. 63-70, 2012.
  18. [18] W. T. Tobler, “A Computer Movie Simulating Urban Growth in the Detroit Region,” Proc. of the Int. Geographical Union Commission on Quantitative Methods, pp. 234-240, 1970.
  19. [19] H. Gao, J. Tang, X. Hu, and H. Liu, “Content-Aware Point of Interest Recommendation on Location-Based Social Networks,” Proc. of the 29th AAAI Conf. on Artificial Intelligence, pp. 1721-1727, 2015.
  20. [20] J. Zhang, P. S. Yu, and X. Kong, “Meta-path Based Multi-network Collective Link Prediction,” Proc. of ACM Int. Conf. on Knowledge Discovery and Data Mining, pp. 1286-1295, 2014.
  21. [21] X. Yu, X. Ren, Y. Sun, Q. Gu, B. Sturt, U. Khandelwal, B. Norick, and J. Han, “Personalized Entity Recommendation: A Heterogeneous Information Network Approach,” Proc. of ACM Int. Conf. on Web Search and Data Mining, pp. 283-292, 2014.
  22. [22] D. A. Freedman, “Statistical Models: Theory and Practice,” 2nd Ed., Cambridge University Press, 2009.

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