PR-RCUC: A POI Recommendation Model Using Region-Based Collaborative Filtering and User-Based Mobile Context

Abstract

Recent years have witnessed the rapid prevalence of big data and it is necessary for mobile application to filter out information for users. As a significant means of information retrieval, recommendation system that recommends a ranked list of items to users according to their preferences has become a key functionality in Location-Based Social Networks (LBSNs). Point of interest (POI) recommendation that aims to recommend satisfactory locations that users may be interested in plays an important role in LBSNs. However, the traditional POI recommendation uses the original user-POI matrix, which faces a huge challenge of data sparsity because most users just check in a few POIs in their phones. Moreover, it is hard for POI recommendation to give reasonable explanations on why user will visit these locations that we recommend. Therefore, in terms of the challenges mentioned above, we propose a new POI recommendation model called PR-RCUC that uses region-based collaborative filtering and user-based mobile context. Firstly, we cluster locations into different regions and enhance the traditional collaborative filtering with region factor. Secondly, we capture the preferences of users on mobile context such as geographical distance and location category. Thirdly, by combing the two parts we present, we finish the final computation of prediction score and recommend Top-K locations to users. The results of experiments on two real-world datasets collected from Foursquare demonstrate the PR-RCUC model outperforms some popular recommendation algorithms and achieves our expected goal.

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References

  1. 1.

    Cheng C, Yang H, King I, Lyu MR (2012) Fused matrix factorization with geographical and social influence in location-based social networks. In: Aaai, vol 12, pp 17–23

  2. 2.

    Fletcher K (2019) Regularizing matrix factorization with implicit user preference embeddings for web API recommendation. In: 2019 IEEE International conference on services computing (SCC), IEEE, pp 1–8

  3. 3.

    Gao H, Duan Y, Shao L, Sun X (2019) Transformation-based processing of typed resources for multimedia sources in the IoT environment. Wirel Netw, pp 1–17

  4. 4.

    Gao H, Huang W, Duan Y (2020) The cloud-edge based dynamic reconfiguration to service workflow for mobile ecommerce environments: A QoS prediction perspective. ACM Transactions on Internet Technology

  5. 5.

    Gao H, Kuang L, Yin Y, Guo B, Dou K (2020) Mining consuming behaviors with temporal evolution for personalized recommendation in mobile marketing Apps. ACM/Springer Mobile Networks and Applications (MONET) 25(4):1233– 1248

    Article  Google Scholar 

  6. 6.

    Gao H, Liu C, Li Y, Yang X (2020) V2VR: Reliable hybrid-network-oriented V2V data transmission and routing considering RSUs and connectivity probability. IEEE Transactions on Intelligent Transportation Systems

  7. 7.

    Gao H, Tang J, Hu X, Liu H, et al. (2015) Content-aware point of interest recommendation on location-based social networks. In: Aaai, vol 15, Citeseer, pp 1721–1727

  8. 8.

    Gao H, Xu Y, Yin Y, Zhang W, Li R, Wang X (2019) Context-aware QoS prediction with neural collaborative filtering for internet-of-things services. IEEE Internet Things J 7(5):4532–4542

    Article  Google Scholar 

  9. 9.

    He J, Li X, Liao L (2017) Category-aware next point-of-interest recommendation via listwise bayesian personalized ranking. In: IJCAI, vol 17, pp 1837–1843

  10. 10.

    He J, Li X, Liao L, Song D, Cheung WK (2016) Inferring a personalized next point-of-interest recommendation model with latent behavior patterns. In: Proceedings of the Thirtieth AAAI conference on artificial intelligence, pp 137–143

  11. 11.

    He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp 173–182

  12. 12.

    Hu S, Tu Z, Wang Z, Xu X (2019) A POI-sensitive knowledge graph based service recommendation method. In: 2019 IEEE International conference on services computing (SCC), IEEE, pp 197–201

  13. 13.

    Li H, Ge Y, Hong R, Zhu H (2016) Point-of-interest recommendations: Learning potential check-ins from friends. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 975–984

  14. 14.

    Li H, Ge Y, Lian D, Liu H (2017) Learning user’s intrinsic and extrinsic interests for point-of-interest recommendation: a unified approach. In: IJCAI, pp 2117–2123

  15. 15.

    Li H, Hong R, Zhu S, Ge Y (2015) Point-of-interest recommender systems: a separate-space perspective. In: 2015 IEEE International conference on data mining, IEEE, pp 231–240

  16. 16.

    Li X, Cong G, Li XL, Pham TAN, Krishnaswamy S (2015) Rank-geoFM: A ranking based geographical factorization method for point of interest recommendation. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, pp 433–442

  17. 17.

    Lian D, Wu Y, Ge Y, Xie X, Chen E (2020) Geography-aware sequential location recommendation. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2009–2019

  18. 18.

    Liu Q, Wu S, Wang L, Tan T (2016) Predicting the next location: a recurrent model with spatial and temporal contexts. In: Thirtieth AAAI conference on artificial intelligence

  19. 19.

    Liu S, Wang L (2018) A self-adaptive point-of-interest recommendation algorithm based on a multi-order markov model. Futur Gener Comput Syst 89:506–514

    Article  Google Scholar 

  20. 20.

    Liu W, Wang ZJ, Yao B, Yin J (2019) Geo-ALM: POI recommendation by fusing geographical information and adversarial learning mechanism. In: IJCAI, pp 1807–1813

  21. 21.

    Liu Y, Wei W, Sun A, Miao C (2014) Exploiting geographical neighborhood characteristics for location recommendation. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management, pp 739–748

  22. 22.

    Manotumruksa J, Macdonald C, Ounis I (2017) A personalised ranking framework with multiple sampling criteria for venue recommendation. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 1469–1478

  23. 23.

    Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2012) BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618

  24. 24.

    Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp 285–295

  25. 25.

    Tran T, Lee K, Liao Y, Lee D (2018) Regularizing matrix factorization with user and item embeddings for recommendation. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 687–696

  26. 26.

    Xue HJ, Dai X, Zhang J, Huang S, Chen J (2017) Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, Melbourne, Australia, pp 3203–3209

  27. 27.

    Yang C, Bai L, Zhang C, Yuan Q, Han J (2017) Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1245–1254

  28. 28.

    Ye M, Yin P, Lee WC, Lee DL (2011) 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

  29. 29.

    Zeng J, He X, Tang H, Wen J (2020) Predicting the next location: a self-attention and recurrent neural network model with temporal context. Transactions on Emerging Telecommunications Technologies, pp e3898

  30. 30.

    Zeng J, Li F, He X, Wen J (2019) Fused collaborative filtering with user preference, geographical and social influence for point of interest recommendation. Int J Web Serv Res (IJWSR) 16(4):40–52

    Article  Google Scholar 

  31. 31.

    Zeng J, Tang H, Li Y, He X (2019) A deep learning model based on sparse matrix for point-of-interest recommendation. In: SEKE, pp 379–492

  32. 32.

    Zhang JD, Chow CY (2015) GeoSoCa: Exploiting geographical, social and categorical correlations for point-of-interest recommendations. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, pp 443–452

  33. 33.

    Zhao K, Zhang Y, Yin H, Wang J, Zheng K, Zhou X, Xing C (2020) Discovering subsequence patterns for next poi recommendation. In: Proceedings of the Twenty-Ninth international joint conference on artificial intelligence, pp 3216–3222

  34. 34.

    Zhao S, Zhao T, King I, Lyu MR (2016) GT-SEER: Geo-temporal sequential embedding rank for point-of-interest recommendation. arXiv:1606.05859

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Acknowledgements

This research is sponsored by Natural Science Foundation of Chongqing, China (No.cstc2020jcyj-msxmX0900) and the Fundamental Research Funds for the Central Universities (Project No.2020CDJ-LHZZ-040).

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Correspondence to Jun Zeng.

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Zeng, J., Tang, H., Zhao, Y. et al. PR-RCUC: A POI Recommendation Model Using Region-Based Collaborative Filtering and User-Based Mobile Context. Mobile Netw Appl (2021). https://doi.org/10.1007/s11036-021-01782-w

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Keywords

  • Recommendation system
  • Point of interest
  • Region
  • Collaborative filtering
  • Mobile context