The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation
Abstract
With the rapid growth of location-based social networks, Point of Interest (POI) recommendation has become an important research problem. However, the scarcity of the check-in data, a type of implicit feedback data, poses a severe challenge for existing POI recommendation methods. Moreover, different types of context information about POIs are available and how to leverage them becomes another challenge. In this paper, we propose a ranking based geographical factorization method, called Rank-GeoFM, for POI recommendation, which addresses the two challenges. In the proposed model, we consider that the check-in frequency characterizes users' visiting preference and learn the factorization by ranking the POIs correctly. In our model, POIs both with and without check-ins will contribute to learning the ranking and thus the data sparsity problem can be alleviated. In addition, our model can easily incorporate different types of context information, such as the geographical influence and temporal influence. We propose a stochastic gradient descent based algorithm to learn the factorization. Experiments on publicly available datasets under both user-POI setting and user-time-POI setting have been conducted to test the effectiveness of the proposed method. Experimental results under both settings show that the proposed method outperforms the state-of-the-art methods significantly in terms of recommendation accuracy.
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Citations
Cites
BPR: Bayesian Personalized Ranking from Implicit Feedback
Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1205.2618 · Cited by 4,242
Probabilistic Matrix Factorization
Mnih, A., & Salakhutdinov, R. (2007) · neural information processing systems · Cited by 3,798
Exploiting geographical influence for collaborative point-of-interest recommendation
Ye, M., Yin, P., Lee, W.-C., & Lee, D.-L. (2011) · Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval · doi:10.1145/2009916.2009962 · Cited by 1,111
Probabilistic Matrix Factorization
Salakhutdinov, R., & Mnih, A. (2007) · Cited by 1,075
Time-aware point-of-interest recommendation
Yuan, Q., Cong, G., Ma, Z., Sun, A., & Thalmann, N. M. (2013) · Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/2484028.2484030 · Cited by 754
Self-citationLessons from the Netflix prize challenge
Bell, R. M., & Koren, Y. (2007) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1345448.1345465 · Cited by 727
Pairwise interaction tensor factorization for personalized tag recommendation
Rendle, S., & Schmidt-Thieme, L. (2010) · Proceedings of the third ACM international conference on Web search and data mining · doi:10.1145/1718487.1718498 · Cited by 675
GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation
Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., & Rui, Y. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623638 · Cited by 608
Recommendations in location-based social networks: a survey
Bao, J., Zheng, Y., Wilkie, D., & Mokbel, M. (2015) · GeoInformatica · doi:10.1007/s10707-014-0220-8 · Cited by 570
Where you like to go next: successive point-of-interest recommendation
Chen, C., Yang, H., Lyu, M. R.-T., & King, I. (2013) · International Joint Conference on Artificial Intelligence · Cited by 529
Cited by
Yang, C., Bai, L., Zhang, C., Yuan, Q., & Han, J. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098094 · Cited by 286
What Your Next Check-in Might Look Like: Next Check-in Behavior Prediction
Sun, H., Cao, C., Chu, X., Hu, T., Lu, J., He, L., Wang, Z., He, H., & Xiong, H. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3625234 · Cited by 10
Metadata
- Authors
- Li, X., Cong, G., Li, X.-L., Pham, T.-A. N., & Krishnaswamy, S.
- Venue
- Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2015-08-09
- Pages
- 433-442
- OpenAlex
- W2044672016
- Cited by
- 340
- References
- 24
- FWCI
- 30.40
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Li, X., Cong, G., Li, X.-L., Pham, T.-A. N., & Krishnaswamy, S. (2015). Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation. Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.1145/2766462.2767722
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