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Slope One Predictors for Online Rating-Based Collaborative Filtering
Open accessAbstract
Rating-based collaborative filtering is the process of predicting how a user would rate a given item from other user ratings. We propose three related slope one schemes with predictors of the form f (x) = x + b, which precompute the average difference between the ratings of one item and another for users who rated both. Slope one algorithms are easy to implement, efficient to query, reasonably accurate, and they support both online queries and dynamic updates, which makes them good candidates for real-world systems. The basic slope one scheme is suggested as a new reference scheme for collaborative filtering. By factoring in items that a user liked separately from items that a user disliked, we achieve results competitive with slower memory-based schemes over the standard benchmark EachMovie and Movielens data sets while better fulfilling the desiderata of CF applications.
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Cited by
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Zheng, Y. (2015) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2015.2465959 · Cited by 454
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Lin, C., Chen, S., Zeng, M., Zhang, S., Gao, M., & Li, H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3183210 · Cited by 62
Metadata
- Authors
- Lemire, D., & Maclachlan, A.
- Venue
- Proceedings of the 2005 SIAM International Conference on Data Mining
- Published
- 2005-01-09
- Pages
- 471-475
- OpenAlex
- W2152208379
- Cited by
- 621
- References
- 0
- FWCI
- 9.61
- Type
- conference paper
- Language
- en
- Open access
- bronze
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