The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Training and testing of recommender systems on data missing not at random
Abstract
Users typically rate only a small fraction of all available items. We show that the absence of ratings carries useful information for improving the top-k hit rate concerning all items, a natural accuracy measure for recommendations. As to test recommender systems, we present two performance measures that can be estimated, under mild assumptions, without bias from data even when ratings are missing not at random (MNAR). As to achieve optimal test results, we present appropriate surrogate objective functions for efficient training on MNAR data. Their main property is to account for all ratings - whether observed or missing in the data. Concerning the top-k hit rate on test data, our experiments indicate dramatic improvements over even sophisticated methods that are optimized on observed ratings only.
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RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
Hand, D. J., & Till, R. J. (2001) · Machine Learning · doi:10.1023/a:1010920819831 · Cited by 2,276
Restricted Boltzmann machines for collaborative filtering
Salakhutdinov, R., Mnih, A., & Hinton, G. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273596 · Cited by 1,885
Cited by
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
He, X., Zhang, H., Kan, M.-Y., & Chua, T.-S. (2016) · Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval · doi:10.1145/2911451.2911489 · Cited by 988
Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization
Hu, Y., Zhang, D., Ye, J., Li, X., & He, X. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.271 · Cited by 829
Bias and Debias in Recommender System: A Survey and Future Directions
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., & He, X. (2023) · ACM Transactions on Information Systems · doi:10.1145/3564284 · Cited by 741
Li, Z., Zhao, Y., Han, J., Su, Y., Jiao, R., Wen, X., & Pei, D. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining · doi:10.1145/3447548.3467075 · Cited by 464
The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables
Lakkaraju, H., Kleinberg, J., Leskovec, J., Ludwig, J., & Mullainathan, S. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098066 · Cited by 138
Metadata
- Authors
- Steck, H.
- Venue
- Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2010-07-25
- Pages
- 713-722
- OpenAlex
- W1992665562
- Cited by
- 365
- References
- 3
- FWCI
- 8.99
- Type
- conference paper
- Language
- en
- Open access
- closed
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