Skip to main content
SpringerLink
Log in

Jointly Modeling Heterogeneous Temporal Properties in Location Recommendation

  • Conference paper
  • First Online:
Database Systems for Advanced Applications (DASFAA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10177))

Included in the following conference series:

Abstract

Point-Of-Interest (POI) recommendation systems suggest interesting locations to users based on their previous check-ins via location-based social networks (LBSNs). Individuals visiting a location are partially affected by many factors including social links, travel distance and the time. A growing line of research has been devoted to taking advantage of various effects to improve existing location recommendation methods. However, the temporal influence owns numerous dimensions which deserve to be explored more in depth. The subset property comprises a set of homogeneous slots such as an hour of the day, the day of the week, week of the month, month of the year, and so on. In addition, time has other attributes such as the recency which signifies the newly visited locations versus others. In this paper, we further study the role of time factor in recommendation models. Accordingly, we define a new problem to jointly model a pair of heterogeneous time-related effects (recency and the subset feature) in location recommendation.

To address the challenges, we propose a generative model which computes the probability for the query user to visit a proposing location based on various homogeneous subset attributes. At the same time, the model calculates how likely the newly visited venues obtain a higher rank compared to others. The model finally performs POI recommendation through combining the effects learned from both homogeneous and heterogeneous temporal influences. Extensive experiments are conducted on two real-life datasets. The results show that our system gains a better effectiveness compared to other competitors in location recommendation.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://en.wikipedia.org/wiki/Geosocial_networking.

  2. 2.

    Photos, Videos, and etc.

References

  1. Cheng, C., Yang, H., King, I., Lyu, M.R.: Fused matrix factorization with geographical and social influence in location-based social networks. In: Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)

    Google Scholar 

  2. Cheng, C., Yang, H., Lyu, M.R., King, I.: Where you like to go next: successive point-of-interest recommendation. IJCAI 13, 2605–2611 (2013)

    Google Scholar 

  3. Das, M., Thirumuruganathan, S., Amer-Yahia, S., Das, G., Yu, C.: An expressive framework and efficient algorithms for the analysis of collaborative tagging. VLDB J. 23(2), 201–226 (2014)

    Article  Google Scholar 

  4. Ding, Y., Li, X.: Time weight collaborative filtering. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 485–492. ACM (2005)

    Google Scholar 

  5. 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 

  6. Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 241–250. ACM (2010)

    Google Scholar 

  7. Hosseini, S., Li, L.T.: Point-of-interest recommendation using temporal orientations of users and locations. In: Navathe, S.B., Wu, W., Shekhar, S., Du, X., Wang, X.S., Xiong, H. (eds.) DASFAA 2016. LNCS, vol. 9642, pp. 330–347. Springer, Cham (2016). doi:10.1007/978-3-319-32025-0_21

    Chapter  Google Scholar 

  8. Hosseini, S., Unankard, S., Zhou, X., Sadiq, S.: Location oriented phrase detection in microblogs. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014. LNCS, vol. 8421, pp. 495–509. Springer, Cham (2014). doi:10.1007/978-3-319-05810-8_33

    Chapter  Google Scholar 

  9. Levandoski, J.J., Sarwat, M., Eldawy, A., Mokbel, M.F.: Lars: a location-aware recommender system. In: 2012 IEEE 28th International Conference on Data Engineering, pp. 450–461. IEEE (2012)

    Google Scholar 

  10. Li, X., Xu, G., Chen, E., Zong, Y.: Learning recency based comparative choice towards point-of-interest recommendation. Expert Syst. Appl. 42(9), 4274–4283 (2015)

    Article  Google Scholar 

  11. Liu, B., Xiong, H., Papadimitriou, S., Fu, Y., Yao, Z.: A general geographical probabilistic factor model for point of interest recommendation. IEEE Trans. Knowl. Data Eng. 27(5), 1167–1179 (2015)

    Article  Google Scholar 

  12. Liu, X., Liu, Y., Aberer, K., Miao, C.: Personalized point-of-interest recommendation by mining users’ preference transition. In: Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, pp. 733–738. ACM (2013)

    Google Scholar 

  13. Wang, W., Yin, H., Chen, L., Sun, Y., Sadiq, S., Zhou, X.: Geo-sage: a geographical sparse additive generative model for spatial item recommendation. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1255–1264. ACM (2015)

    Google Scholar 

  14. Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., Sun, J.: Temporal recommendation on graphs via long-and short-term preference fusion. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 723–732. ACM (2010)

    Google Scholar 

  15. Ye, M., Liu, X., Lee, W.-C.: Exploring social influence for recommendation: a generative model approach. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 671–680. ACM (2012)

    Google Scholar 

  16. Ye, M., Yin, P., Lee, W.-C., Lee, D.-L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 325–334. ACM (2011)

    Google Scholar 

  17. Yin, H., Cui, B.: Location-based and real-time recommendation. In: Spatio-Temporal Recommendation in Social Media, pp. 65–98. Springer, Singapore (2016)

    Google Scholar 

  18. Yin, H., Cui, B., Huang, Z., Wang, W., Wu, X., Zhou, X.: Joint modeling of users’ interests and mobility patterns for point-of-interest recommendation. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 819–822. ACM (2015)

    Google Scholar 

  19. Yin, H., Cui, B., Sun, Y., Hu, Z., Chen, L.: Lcars: a spatial item recommender system. ACM Trans. Inf. Syst. (TOIS) 32(3), 11 (2014)

    Article  Google Scholar 

  20. Yin, H., Sun, Y., Cui, B., Hu, Z., Chen, L.: Lcars: a location-content-aware recommender system. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 221–229. ACM (2013)

    Google Scholar 

  21. Yin, H., Zhou, X., Shao, Y., Wang, H., Sadiq, S.: Joint modeling of user check-in behaviors for point-of-interest recommendation. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1631–1640. ACM (2015)

    Google Scholar 

  22. Yuan, Q., Cong, G., Ma, Z., Sun, A., Thalmann, N.M.: Time-aware point-of-interest recommendation. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 363–372. ACM (2013)

    Google Scholar 

  23. 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 

  24. Zhang, J.-D., Chow, C.-Y.: Ticrec: a probabilistic framework to utilize temporal influence correlations for time-aware location recommendations

    Google Scholar 

  25. Zhang, J.-D., Chow, C.-Y., Li, Y.: igeorec: a personalized and efficient geographical location recommendation framework. IEEE Trans. Serv. Comput. 8(5), 701–714 (2015)

    Article  Google Scholar 

  26. Zhang, W., Wang, J.: Location and time aware social collaborative retrieval for new successive point-of-interest recommendation. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1221–1230. ACM (2015)

    Google Scholar 

  27. Zhang, Y., Zhang, M., Zhang, Y., Lai, G., Liu, Y., Zhang, H., Ma, S.: Daily-aware personalized recommendation based on feature-level time series analysis. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1373–1383. International World Wide Web Conferences Steering Committee (2015)

    Google Scholar 

  28. Zhao, S., Zhao, T., King, I., Lyu, M.R.: Gt-seer: geo-temporal sequential embedding rank for point-of-interest recommendation. arXiv preprint arXiv:1606.05859 (2016)

Download references

Acknowledgement

Meihui Zhang was supported by SUTD Start-up Research Grant under Project No. SRG ISTD 2014 084. The work is also partially supported by ARC Discovery Early Career Researcher Award (DE160100308) and ARC Discovery Project (DP170103954).

Author information

Authors and Affiliations

  1. The University of Queensland, Brisbane, Australia

    Saeid Hosseini, Hongzhi Yin, Xiaofang Zhou & Shazia Sadiq

  2. Singapore University of Technology and Design, Singapore, Singapore

    Meihui Zhang

Authors
  1. Saeid Hosseini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongzhi Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Meihui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaofang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shazia Sadiq
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saeid Hosseini .

Editor information

Editors and Affiliations

  1. Arizona State University , Tempe - Phoenix, Arizona, USA

    Selçuk Candan

  2. Hong Kong University of Science and Tech , Hong Kong, China

    Lei Chen

  3. Aalborg University , Aalborg, Denmark

    Torben Bach Pedersen

  4. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

  5. The University of Queensland , Brisbane, Queensland, Australia

    Wen Hua

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Hosseini, S., Yin, H., Zhang, M., Zhou, X., Sadiq, S. (2017). Jointly Modeling Heterogeneous Temporal Properties in Location Recommendation. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10177. Springer, Cham. https://doi.org/10.1007/978-3-319-55753-3_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-55753-3_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55752-6

  • Online ISBN: 978-3-319-55753-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation