skip to main content
10.1145/2939672.2939773acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

Unified Point-of-Interest Recommendation with Temporal Interval Assessment

Published:13 August 2016Publication History

ABSTRACT

Point-of-interest (POI) recommendation, which helps mobile users explore new places, has become an important location-based service. Existing approaches for POI recommendation have been mainly focused on exploiting the information about user preferences, social influence, and geographical influence. However, these approaches cannot handle the scenario where users are expecting to have POI recommendation for a specific time period. To this end, in this paper, we propose a unified recommender system, named the 'Where and When to gO' (WWO) recommender system, to integrate the user interests and their evolving sequential preferences with temporal interval assessment. As a result, the WWO system can make recommendations dynamically for a specific time period and the traditional POI recommender system can be treated as the special case of the WWO system by setting this time period long enough. Specifically, to quantify users' sequential preferences, we consider the distributions of the temporal intervals between dependent POIs in the historical check-in sequences. Then, to estimate the distributions with only sparse observations, we develop the low-rank graph construction model, which identifies a set of bi-weighted graph bases so as to learn the static user preferences and the dynamic sequential preferences in a coherent way. Finally, we evaluate the proposed approach using real-world data sets from several location-based social networks (LBSNs). The experimental results show that our method outperforms the state-of-the-art approaches for POI recommendation in terms of various metrics, such as F-measure and NDCG, with a significant margin.

References

  1. John Canny. Gap: a factor model for discrete data. In SIGIR, pages 122--129. ACM, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Zaiben Chen, Heng Tao Shen, and Xiaofang Zhou. Discovering popular routes from trajectories. In ICDE, pages 900--911. IEEE, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Chen Cheng, Haiqin Yang, Irwin King, and Michael R Lyu. Fused matrix factorization with geographical and social influence in location-based social networks. In AAAI, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Chen Cheng, Haiqin Yang, Michael R Lyu, and Irwin King. Where you like to go next: Successive point-of-interest recommendation. In IJCAI, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Eunjoon Cho, Seth A Myers, and Jure Leskovec. Friendship and mobility: user movement in location-based social networks. In KDD, pages 1082--1090, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, and Quan Yuan. Personalized ranking metric embedding for next new poi recommendation. In IJCAI, pages 2069--2075. AAAI Press, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu. Exploring temporal effects for location recommendation on location-based social networks. In RecSys, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Yong Ge, Qi Liu, Hui Xiong, Alexander Tuzhilin, and Jian Chen. Cost-aware travel tour recommendation. In KDD, pages 983--991. ACM, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Marta C Gonzalez, Cesar A Hidalgo, and Albert-Laszlo Barabasi. Understanding individual human mobility patterns. Nature, 453 (7196): 779--782, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  10. Kalervo Järvelin and Jaana Kekäläinen. Cumulated gain-based evaluation of ir techniques. TOIS, 20 (4): 422--446, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Defu Lian, Cong Zhao, Xing Xie, Guangzhong Sun, Enhong Chen, and Yong Rui. Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation. In KDD, pages 831--840, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Bin Liu and Hui Xiong. Point-of-interest recommendation in location based social networks with topic and location awareness. In SDM, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  13. Bin Liu, Hui Xiong, Spiros Papadimitriou, Yanjie Fu, and Zijun Yao. A general geographical probabilistic factor model for point of interest recommendation. TKDE, 27 (5): 1167--1179, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Chuanren Liu, Fei Wang, Jianying Hu, and Hui Xiong. Temporal phenotyping from longitudinal electronic health records: A graph based framework. In KDD, pages 705--714. ACM, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Chuanren Liu, Kai Zhang, Hui Xiong, Guofei Jiang, and Qiang Yang. Temporal skeletonization on sequential data: patterns, categorization, and visualization. TKDE, 28 (1): 211--223, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Qi Liu, Enhong Chen, Hui Xiong, Yong Ge, Zhongmou Li, and Xiang Wu. A cocktail approach for travel package recommendation. TKDE, 26 (2): 278--293, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Yanchi Liu, Chuanren Liu, Nicholas Jing Yuan, Lian Duan, Yanjie Fu, Hui Xiong, Songhua Xu, and Junjie Wu. Intelligent bus routing with heterogeneous human mobility patterns. KAIS, pages 1--33, 2016.Google ScholarGoogle Scholar
  18. Andriy Mnih and Ruslan Salakhutdinov. Probabilistic matrix factorization. In NIPS, pages 1257--1264, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Anastasios Noulas, Salvatore Scellato, Neal Lathia, and Cecilia Mascolo. Mining user mobility features for next place prediction in location-based services. In ICDM, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. Factorizing personalized markov chains for next-basket recommendation. In WWW, pages 811--820, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Peter Richtárik and Martin Takáč. Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function. Math. Prog., 144 (1--2): 1--38, 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Jitao Sang, Tao Mei, Jian-Tao Sun, Changsheng Xu, and Shipeng Li. Probabilistic sequential pois recommendation via check-in data. In GIS, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Chaoming Song, Zehui Qu, Nicholas Blumm, and Albert-László Barabási. Limits of predictability in human mobility. Science, 327 (5968): 1018--1021, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  24. Jian Wang and Yi Zhang. Opportunity model for e-commerce recommendation: right product; right time. In SIGIR, pages 303--312. ACM, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Jingyuan Yang, Chuanren Liu, Mingfei Teng, Hui Xiong, March Liao, and Vivian Zhu. Exploiting temporal and social factors for b2b marketing campaign recommendations. In ICDM, pages 499--508. IEEE, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Ghim-Eng Yap, Xiao-Li Li, and S Yu Philip. Effective next-items recommendation via personalized sequential pattern mining. In Database Systems for Advanced Applications, pages 48--64. Springer, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik-Lun Lee. Exploiting geographical influence for collaborative point-of-interest recommendation. In SIGIR, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Zhiwen Yu, Huang Xu, Zhe Yang, and Bin Guo. Personalized travel package with multi-point-of-interest recommendation based on crowdsourced user footprints. IEEE T HUM-MACH SYST, 46 (1), 2015.Google ScholarGoogle Scholar
  29. Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat Thalmann. Time-aware point-of-interest recommendation. In SIGIR, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Jia-Dong Zhang and Chi-Yin Chow. Spatiotemporal sequential influence modeling for location recommendations: A gravity-based approach. TIST, 7 (1): 11, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Jia-Dong Zhang, Chi-Yin Chow, and Yanhua Li. Lore: Exploiting sequential influence for location recommendations. In GIS, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Gang Zhao, Mong Li Lee, Wynne Hsu, and Wei Chen. Increasing temporal diversity with purchase intervals. In SIGIR, pages 165--174, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Vincent Wenchen Zheng, Bin Cao, Yu Zheng, Xing Xie, and Qiang Yang. Collaborative filtering meets mobile recommendation: A user-centered approach. In AAAI, 2010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Hengshu Zhu, Enhong Chen, Hui Xiong, Kuifei Yu, Huanhuan Cao, and Jilei Tian. Mining mobile user preferences for personalized context-aware recommendation. TIST, 5 (4): 58, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Unified Point-of-Interest Recommendation with Temporal Interval Assessment

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
            August 2016
            2176 pages
            ISBN:9781450342322
            DOI:10.1145/2939672

            Copyright © 2016 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 13 August 2016

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article

            Acceptance Rates

            KDD '16 Paper Acceptance Rate66of1,115submissions,6%Overall Acceptance Rate1,133of8,635submissions,13%

            Upcoming Conference

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader