The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
LCARS: a location-content-aware recommender system
Abstract
Newly emerging location-based and event-based social network services provide us with a new platform to understand users' preferences based on their activity history. A user can only visit a limited number of venues/events and most of them are within a limited distance range, so the user-item matrix is very sparse, which creates a big challenge for traditional collaborative filtering-based recommender systems. The problem becomes more challenging when people travel to a new city where they have no activity history.
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Cites
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Item-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
Amazon.com recommendations: item-to-item collaborative filtering
Linden, G., Smith, B., & York, J. (2003) · IEEE Internet Computing · doi:10.1109/mic.2003.1167344 · Cited by 5,403
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Koren, Y. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401944 · Cited by 3,852
(2011) · Springer eBooks · doi:10.1007/978-0-387-85820-3 · Cited by 3,761
Friendship and mobility: user movement in location-based social networks
Cho, E., Myers, S. A., & Leskovec, J. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020579 · Cited by 2,903
ArnetMiner: extraction and mining of academic social networks
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1402008 · Cited by 2,231
Performance of recommender algorithms on top-n recommendation tasks
Cremonesi, P., Koren, Y., & Turrin, R. (2010) · Proceedings of the fourth ACM conference on Recommender systems · doi:10.1145/1864708.1864721 · Cited by 1,410
Factorization Machines with libFM
Rendle, S. (2012) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2168752.2168771 · Cited by 1,337
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
Cited by
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
Metadata
- Authors
- Yin, H., Sun, Y., Cui, B., Hu, Z., & Chen, L.
- Venue
- Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2013-08-11
- Pages
- 221-229
- OpenAlex
- W2149814409
- Cited by
- 331
- References
- 31
- FWCI
- 31.21
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yin, H., Sun, Y., Cui, B., Hu, Z., & Chen, L. (2013). LCARS: a location-content-aware recommender system. Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/2487575.2487608
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
