瓶颈
计算机科学
推荐系统
信息瓶颈法
代表(政治)
过程(计算)
组分(热力学)
数据挖掘
机器学习
人工智能
相互信息
政治
热力学
操作系统
物理
嵌入式系统
法学
政治学
作者
Dugang Liu,Pengxiang Cheng,Hong Zhu,Zhenhua Dong,Xiuqiang He,Weike Pan,Zhong Ming
标识
DOI:10.1145/3460231.3474263
摘要
How to effectively mitigate the bias of feedback in recommender systems is an important research topic. In this paper, we first describe the generation process of the biased and unbiased feedback in recommender systems via two respective causal diagrams, where the difference between them can be regarded as the source of bias. We then define this difference as a confounding bias, which can be regarded as a collection of some specific biases that have previously been studied. For the case with biased feedback alone, we derive the conditions that need to be satisfied to obtain a debiased representation from the causal diagrams. Based on information theory, we propose a novel method called debiased information bottleneck (DIB) to optimize these conditions and then find a tractable solution for it. In particular, the proposed method constrains the model to learn a biased embedding vector with independent biased and unbiased components in the training phase, and uses only the unbiased component in the test phase to deliver more accurate recommendations. Finally, we conduct extensive experiments on a public dataset and a real product dataset to verify the effectiveness of the proposed method and discuss its properties.
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