A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data

Pengxiang Cheng
Pengxiang Cheng
Weike Pan
Weike Pan
Zhong Ming
Zhong Ming

SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval Virtual Event China July, 2020, pp. 831-840, 2020.

Cited by: 0|Bibtex|Views91|DOI:https://doi.org/10.1145/3397271.3401083
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Other Links: dl.acm.org|dblp.uni-trier.de|academic.microsoft.com

Abstract:

Recommender systems are feedback loop systems, which often face bias problems such as popularity bias, previous model bias and position bias. In this paper, we focus on solving the bias problems in a recommender system via a uniform data. Through empirical studies in online and offline settings, we observe that simple modeling with a unif...More

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