The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Learning Graph-based POI Embedding for Location-based Recommendation
Abstract
With the rapid prevalence of smart mobile devices and the dramatic proliferation of location-based social networks (LBSNs), location-based recommendation has become an important means to help people discover attractive and interesting points of interest (POIs). However, the extreme sparsity of user-POI matrix and cold-start issue create severe challenges, causing CF-based methods to degrade significantly in their recommendation performance. Moreover, location-based recommendation requires spatiotemporal context awareness and dynamic tracking of the user's latest preferences in a real-time manner.
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Cites
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Cited by
Network Representation Learning: A Survey
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Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation
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Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach
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Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation
Zhao, P., Zhu, H., Liu, Y., Xu, J., Li, Z., Zhuang, F., Sheng, V. S., & Zhou, X. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33015877 · Cited by 283
Metadata
- Authors
- Xie, M., Yin, H., Wang, H., Xu, F., Chen, W., & Wang, S.
- Venue
- Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2016-10-24
- Pages
- 15-24
- OpenAlex
- W2534727297
- Cited by
- 391
- References
- 26
- FWCI
- 35.61
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Xie, M., Yin, H., Wang, H., Xu, F., Chen, W., & Wang, S. (2016). Learning Graph-based POI Embedding for Location-based Recommendation. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. https://doi.org/10.1145/2983323.2983711
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