The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation
Abstract
Recommender system is one of the most popular data mining topics that keep drawing extensive attention from both academia and industry. Among them, POI (point of interest) recommendation is extremely practical but challenging: it greatly benefits both users and businesses in real-world life, but it is hard due to data scarcity and various context. While a number of algorithms attempt to tackle the problem w.r.t. specific data and problem settings, they often fail when the scenarios change. In this work, we propose to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs. To enable such a framework, we develop PACE (Preference And Context Embedding), a deep neural architecture that jointly learns the embeddings of users and POIs to predict both user preference over POIs and various context associated with users and POIs. We show that PACE successfully bridges CF (collaborative filtering) and SSL by generalizing the de facto methods matrix factorization of CF and graph Laplacian regularization of SSL. Extensive experiments on two real location-based social network datasets demonstrate the effectiveness of PACE.
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Cites
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Neural Collaborative Filtering
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(2013) · Cited by 5,726
Cited by
Deep Learning Based Recommender System: A Survey and New Perspectives
Zhang, S., Yao, L., Sun, A., & Tay, Y. (2020) · ACM Computing Surveys · doi:10.1145/3285029 · Cited by 1,511
Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation
Zhao, P., Luo, A., Liu, Y., Xu, J., Li, Z., Zhuang, F., Sheng, V. S., & Zhou, X. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3007194 · Cited by 357
Graph-Based Semi-Supervised Learning: A Comprehensive Review
Song, Z., Yang, X., Xu, Z., & King, I. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3155478 · Cited by 334
Metadata
- Authors
- Yang, C., Bai, L., Zhang, C., Yuan, Q., & Han, J.
- Venue
- Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2017-08-04
- Pages
- 1245-1254
- OpenAlex
- W2743289629
- Cited by
- 286
- References
- 57
- FWCI
- 33.25
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yang, C., Bai, L., Zhang, C., Yuan, Q., & Han, J. (2017). Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3097983.3098094
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