Advances in Theories, Methods and Philosophy
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Paper Number
2583
Paper Type
Completed
Description
Conventional recommender systems (RSs) rely on consumers’ feedback like product ratings to elicit parameters for personalized recommendations. Such an approach suffers severely from the biases caused by consumers’ self-selection behaviors. RSs fed with biased input may reinforce the biases and result in biased models that are incapable to effectively predict consumer preferences. By examining the holistic process of consumer purchase and rating, three types of self-selection biases, i.e., the exposure, acquisition and under-report biases, are considered. To mitigate these biases in training RSs, we propose a generative modeling approach that jointly incorporates consumer behavioral patterns in the product exposure, purchase, and rating stages. To rigorously evaluate the performance of the proposed approach, two bias-free datasets are used as testbeds. The experimental results show that the proposed approach outperforms state-of-the-art methods. Our research contributes to the literature and practice of RSs by providing an innovative debiasing approach with biased inputs.
Recommended Citation
Shi, Yansong; Wang, Cong; Guo, Xunhua; and Chen, Guoqing, "Training Personalized Recommender Systems with Biased Data: A Joint Likelihood Approach to Modeling Consumer Self-selection Behaviors" (2021). ICIS 2021 Proceedings. 11.
https://aisel.aisnet.org/icis2021/adv_in_theories/adv_in_theories/11
Training Personalized Recommender Systems with Biased Data: A Joint Likelihood Approach to Modeling Consumer Self-selection Behaviors
Conventional recommender systems (RSs) rely on consumers’ feedback like product ratings to elicit parameters for personalized recommendations. Such an approach suffers severely from the biases caused by consumers’ self-selection behaviors. RSs fed with biased input may reinforce the biases and result in biased models that are incapable to effectively predict consumer preferences. By examining the holistic process of consumer purchase and rating, three types of self-selection biases, i.e., the exposure, acquisition and under-report biases, are considered. To mitigate these biases in training RSs, we propose a generative modeling approach that jointly incorporates consumer behavioral patterns in the product exposure, purchase, and rating stages. To rigorously evaluate the performance of the proposed approach, two bias-free datasets are used as testbeds. The experimental results show that the proposed approach outperforms state-of-the-art methods. Our research contributes to the literature and practice of RSs by providing an innovative debiasing approach with biased inputs.
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