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Methods and metrics for cold-start recommendations
Open accessAbstract
We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a naïve Bayes classifier on the cold-start problem, where we wish to recommend items that no one in the community has yet rated. We systematically explore three testing methodologies using a publicly available data set, and explain how these methods apply to specific real-world applications. We advocate heuristic recommenders when benchmarking to give competent baseline performance. We introduce a new performance metric, the CROC curve, and demonstrate empirically that the various components of our testing strategy combine to obtain deeper understanding of the performance characteristics of recommender systems. Though the emphasis of our testing is on cold-start recommending, our methods for recommending and evaluation are general.
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Geng, S., Liu, S., Fu, Z., Ge, Y., & Zhang, Y. (2022) · Proceedings of the 16th ACM Conference on Recommender Systems · doi:10.1145/3523227.3546767 · Cited by 556
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Lin, Z., Lyu, S., Cao, H., Xu, F., Wei, Y., Samet, H., & Li, Y. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3432229 · Cited by 18
Metadata
- Authors
- Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M.
- Venue
- Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2002-08-11
- Pages
- 253-260
- OpenAlex
- W2158515176
- Cited by
- 1,780
- References
- 0
- FWCI
- 14.54
- Type
- conference paper
- Language
- en
- Open access
- green
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