Skip to main content

Advertisement

Springer Nature Link
Log in

Recommendations based on user effective point-of-interest path

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Point-of-interest (POI) recommendation has become an important service in location-based social networks. Existing recommendation algorithms provide users with a diverse pool of POIs. However, these algorithms tend to generate a list of unrelated POIs that user cannot continuously visit due to lack of appropriate associations. In this paper, we first proposed a concept that can recommend POIs by considering both category diversity features of POIs and possible associations of POIs. Then, we developed a top-k POI recommendation model based on effective path coverage. Moreover, considering this model has been proven to be a NP-hard problem, we developed a dynamic optimization algorithm to provide an approximate solution. Finally, we compared it with two popular algorithms by using two real-world datasets, and found that our proposed algorithm has better performance in terms of diversity and precision.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Liu B, Fu Y, Yao Z et al (2013) Learning geographical preferences for point-of-interest recommendation. In: ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1043–1051

  2. Ye M, Yin P, Lee WC (2010) Location recommendation for location-based social networks. In: ACM Sigspatial international symposium on advances in geographic information systems, Acm-Gis 2010, November 3–5, 2010, San Jose, CA, USA, Proceedingd. DBLP, pp 458–461

  3. Ye M, Shou D, Lee WC et al (2011) On the semantic annotation of places in location-based social networks. In: ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 520–528

  4. Yuan Q, Cong G, Ma Z et al (2013) Time-aware pointof-interest recommendation. In: International ACM SIGIRConference on research and development in information retrieval. ACM, pp 363–372

  5. Wang B, Ester M, Bu J et al (2014) Who also likes it? generating the most persuasive social explanations in recommender systems. Twenty-Eighth AAAI Conference on Artificial Intelligence. AAAI Press 173–179

  6. Qiao X, Yu W, Zhang J et al (2015) Recommending nearby strangers instantly based on similar check-in behaviors. IEEE Trans Autom Sci Eng 12(3):1114–1124

    Article  Google Scholar 

  7. Ye M, Liu X, Lee WC (2012) 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. ACM, pp 671–680

  8. Chen X, Zeng Y, Cong G, Qin S, Xiang Y, Dai Y (2015) On information coverage for location category based point-of-interest recommendation. In: AAAI, pp 37–43

  9. Lu Z, Dou Z, Lian J, Xie X, Yang Q (2015) Content-based collaborative filtering for news topic recommendation. In: AAAI, pp 217–223

  10. Wang J, Yin J (2014) Combining user-based and item-based collaborative filtering techniques to improve recommendation diversity. In: International conference on biomedical engineering and informatics. IEEE, Piscataway, pp 661–665

  11. Zhang C, Wang K, Lim EP et al (2015) Are features equally representative? A feature-centric recommendation. In: Twenty-ninth AAAI conference on artificial intelligence. AAAI Press, New Orleans, pp 389–395

  12. Cai Y, Leung HF, Li Q et al (2014) Typicality-based collaborative filtering recommendation. IEEE Trans Knowl Data Eng 26(3):766–779

    Article  Google Scholar 

  13. Liu Q, Wu S, Wang L (2015) COT: contextual operating tensor for context-aware recommender systems. In: Twenty-ninth AAAI conference on artificial intelligence. AAAI Press, New Orleans, pp 203–209

  14. Chen C, Zheng X, Wang Y et al (2014) Context-aware collaborative topic regression with social matrix factorization for recommender systems. In: Twenty-eighth AAAI conference on artificial intelligence. AAAI Press, New Orleans, pp 9–15

  15. Fang H, Bao Y, Zhang J (2014) Leveraging decomposed trust in probabilistic matrix factorization for effective recommendation. In: Twenty-eighth AAAI conference on artificial intelligence. AAAI Press, New Orleans, pp 30–36

  16. Chen J, Wang C, Wang J (2015) A personalized interest forgetting markov model for recommendations. In: AAAI conference on artificial intelligence

  17. Bao Y, Fang H, Zhang J (2014) TopicMF: simultaneously exploiting ratings and reviews for recommendation. In: Twenty-eighth AAAI conference on artificial intelligence. AAAI Press, New Orleans, pp 2–8

  18. Hao F, Li S, Min G et al (2015) An efficient approach to generating location-sensitive recommendations in adhoc social network environments. IEEE Trans Serv Comput 8(3):520–533

    Article  Google Scholar 

  19. Zhang JD, Chowmember CY, Li Y (2015) iGeoRec: a personalized and efficient geographical location recommendation framework. Serv Comput IEEE Trans 8(5):701–714

    Article  Google Scholar 

  20. Qiao Z, Zhang P, Cao Y et al (2014) Combining heterogenous social and geographical information for eventrecommendation. In: Twenty-eighth AAAI conference on artificial intelligence. AAAI Press, New Orleans, pp 145–151

  21. Ye M, Yin P, Lee WC et al (2011) Exploiting geographical influence for collaborative point-of-interest recommendation. In: International Acm Sigir conference on research and development in information retrieval. ACM, New Orleans, pp 325–334

  22. Yuan Q, Cong G, Sun A (2014) Graph-based Point-ofinterest Recommendation with Geographical and Temporal Influences. In: ACM International conference on conference on information and knowledge management. ACM, New Orleans, pp 659–668

  23. Chen KH (2014) User clustering based social network recommendation. Chin J Comput 36(2):349–359

    Article  Google Scholar 

  24. Yang Y, Ma Z, Yang Y et al (2015) Multitask spectral clustering by exploring intertask correlation. IEEE Trans Cybern 45(5):1083–1094

    Article  Google Scholar 

  25. Zhang HS et al (2011) Group interests and their correlations mining based on wikipedia. Chin J Comput 34(11):2234–2242

    Article  Google Scholar 

  26. Zhang CS, Yan L (2014) Extension of local association rules mining algorithm based on apriori algorithm. In: IEEE international conference on software engineering and service science. IEEE, Piscataway, pp 340–343

  27. Garg M, Kumar K, Garg R (2012) A modern search technique for frequent itemset using FP tree

  28. Khuller S, Moss A, Naor J (1998) The budgeted maximum coverage problem. Inf Process Lett 70(1):39–45

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science, Nanjing University of Posts and Telecommunication, Nanjing, 210003, People’s Republic of China

    Guoqiang Zhou, Shuai Zhang, Yi Fan, Jingjin Li, Wenbo Yao & Hongfang Liu

Authors
  1. Guoqiang Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Shuai Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yi Fan
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jingjin Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Wenbo Yao
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Hongfang Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Guoqiang Zhou.

Ethics declarations

Conflict of interest

We declare that we have no conflict of interest.

Human and animal rights statement

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, G., Zhang, S., Fan, Y. et al. Recommendations based on user effective point-of-interest path. Int. J. Mach. Learn. & Cyber. 10, 2887–2899 (2019). https://doi.org/10.1007/s13042-018-00910-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-018-00910-5

Keywords