Cited By
View all- Zhang SJiang TKuang KFeng FYu JMa JZhao ZZhu JYang HChua TWu F(2023)SLED: Structure Learning based Denoising for RecommendationACM Transactions on Information Systems10.1145/361138542:2(1-31)Online publication date: 8-Nov-2023
CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
Authors: 
Jiawei Chen, 
Chengquan Jiang, Can Wang, Sheng Zhou, Yan Feng, Chun Chen, Martin Ester, 
Xiangnan HeAuthors Info & Claims
Jiawei Chen, Chengquan Jiang, Can Wang, Sheng Zhou, Yan Feng, Chun Chen, Martin Ester, Xiangnan HeAuthors Info & ClaimsArticle No.: 34, Pages 1 - 24
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.
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Index Terms
- CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
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Publication History
Published: 25 May 2021
Accepted: 01 February 2021
Revised: 01 December 2020
Received: 01 June 2020
Published in TOIS Volume 39, Issue 3
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- National Natural Science Foundation of China
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View all- Zhang SJiang TKuang KFeng FYu JMa JZhao ZZhu JYang HChua TWu F(2023)SLED: Structure Learning based Denoising for RecommendationACM Transactions on Information Systems10.1145/361138542:2(1-31)Online publication date: 8-Nov-2023
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