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CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation.
- Source :
-
ACM Transactions on Information Systems . 2021, Vol. 39 Issue 3, p1-24. 24p. - Publication Year :
- 2021
-
Abstract
- Sampling strategies have been widely applied in many recommendation systems to accelerate model learning from implicit feedback data. A typical strategy is to draw negative instances with uniform distribution, which, however, will severely affect a model's convergence, stability, and even recommendation accuracy. A promising solution for this problem is to over-sample the "difficult" (a.k.a. informative) instances that contribute more on training. But this will increase the risk of biasing the model and leading to non-optimal results. Moreover, existing samplers are either heuristic, which require domain knowledge and often fail to capture real "difficult" instances, or rely on a sampler model that suffers from low efficiency. To deal with these problems, we propose CoSam, an efficient and effective collaborative sampling method that consists of (1) a collaborative sampler model that explicitly leverages user-item interaction information in sampling probability and exhibits good properties of normalization, adaption, interaction information awareness, and sampling efficiency, and (2) an integrated sampler-recommender framework, leveraging the sampler model in prediction to offset the bias caused by uneven sampling. Correspondingly, we derive a fast reinforced training algorithm of our framework to boost the sampler performance and sampler-recommender collaboration. Extensive experiments on four real-world datasets demonstrate the superiority of the proposed collaborative sampler model and integrated sampler-recommender framework. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10468188
- Volume :
- 39
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- ACM Transactions on Information Systems
- Publication Type :
- Academic Journal
- Accession number :
- 152866583
- Full Text :
- https://doi.org/10.1145/3450289