CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation

Jiawei Chen

doi:10.1145/3450289

CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation

2021-05-25

2

OA

AI

DOI：10.1145/3450289

Save

View PDF

Abstract

En 中文

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.

Keywords:

Sampling

recommendation

efficiency

adaption

CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation

Abstract

AI Summary

Journal

Organization