The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization
Open accessAbstract
Real-world relational data are seldom stationary, yet traditional collaborative filtering algorithms generally rely on this assumption. Motivated by our sales prediction problem, we propose a factor-based algorithm that is able to take time into account. By introducing additional factors for time, we formalize this problem as a tensor factorization with a special constraint on the time dimension. Further, we provide a fully Bayesian treatment to avoid tuning parameters and achieve automatic model complexity control. To learn the model we develop an efficient sampling procedure that is capable of analyzing large-scale data sets. This new algorithm, called Bayesian Probabilistic Tensor Factorization (BPTF), is evaluated on several real-world problems including sales prediction and movie recommendation. Empirical results demonstrate the superiority of our temporal model.
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Cited by
Tensor Decomposition for Signal Processing and Machine Learning
Sidiropoulos, N. D., De Lathauwer, L., Fu, X., Huang, K., Papalexakis, E. E., & Faloutsos, C. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2017.2690524 · Cited by 1,617
Karatzoglou, A., Amatriain, X., Baltrunas, L., & Oliver, N. (2010) · Proceedings of the fourth ACM conference on Recommender systems · doi:10.1145/1864708.1864727 · Cited by 721
Time Interval Aware Self-Attention for Sequential Recommendation
Li, J., Wang, Y., & McAuley, J. (2020) · Proceedings of the 13th International Conference on Web Search and Data Mining · doi:10.1145/3336191.3371786 · Cited by 649
Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination
Zhao, Q., Zhang, L., & Cichocki, A. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2392756 · Cited by 587
A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation
Chen, X., He, Z., & Sun, L. (2019) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.11.003 · Cited by 373
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Metadata
- Authors
- Xiong, L., Chen, X., Huang, T.-K., Schneider, J., & Carbonell, J. G.
- Venue
- Proceedings of the 2010 SIAM International Conference on Data Mining
- Published
- 2010-04-29
- Pages
- 211-222
- OpenAlex
- W1500188831
- Cited by
- 585
- References
- 25
- FWCI
- 12.14
- Type
- conference paper
- Language
- en
- Open access
- green
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