[Objective] This study is to address the issues of data sparsity and user selection bias in explicit rating data in recommender systems, by proposing a rating data filling model based on uninteresting item injection. [Methods] A general rating data filling model is constructed based on Conditional Generative Adversarial Networks framework. Denoising Auto-Encoder is used as the generator to capture the nonlinear potential factors behind the interaction and improve the robustness of model. To address the selection bias problem, uninteresting items are identified based on the user’s time point visibility, and are injected into the model by modifying the mask operation to generate data consistent with the user’s real rating distribution. [Results] Our experiments on MovieLens and Amazon datasets show that after data filling, the recommendation accuracy of ItemCF, BiasSVD, and AutoRec improves by more than three times on average. [Limitations] The data generation method relies on rating data and may not be effective in the case of extremely sparse rating data, such as in cold start scenarios. [Conclusions] The proposed model effectively alleviates data sparsity and eliminates selection bias, significantly improving the performance of recommended tasks of existing collaborative filtering methods.
石磊, 李树青, 蒋明锋, 张志旺, 王愈. 融合选择数据偏差消除和条件生成对抗网络的显式评分填充策略*[J]. 数据分析与知识发现, 2023, 7(6): 1-14.
Shi Lei, Li Shuqing, Jiang Mingfeng, Zhang Zhiwang, Wang Yu. Explicit Rating Filling Strategy Based on Selection Data Bias Elimination and Conditional Generative Adversarial Networks. Data Analysis and Knowledge Discovery, 2023, 7(6): 1-14.
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