Popularity bias is a common phenomenon in the user-item interaction, which means a user interacts with the items just because of the items’ popularity, but the user does not actually interest in these items. Neglecting popularity bias in recommendation systems can result in favoring popular items over personal preferences. This article proposes a new recommendation framework for enhancing the D is E ntanglement of popularity bias based on C ontrastive L earning ( DECL ). In DECL, the interest and conformity representation sets of the users and the items are generated through a disentangled representation learning process. A contrastive learning process is then performed to optimize the distributions of the disentangled sets in the representation space. A customized loss function is designed to facilitate the parameter optimization, and the final recommendation is made based on the interest and conformity. Extensive experiments and comparison studies are conducted on three real-world datasets to validate the effectiveness of the proposed DECL framework. The experiment results show that compared with the state-of-the-art methods, DECL can achieve up to 10.69% performance improvement on the Ciao dataset. This indicates the proposed system can effectively disentangle popularity bias in recommendation and has large application potential.

中文翻译：

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通过三元组对比学习增强推荐的流行度偏差


流行度偏差是用户与物品交互中的一种常见现象，即用户只是因为物品的受欢迎程度而与物品进行交互，但用户实际上并不对这些物品感兴趣。忽视推荐系统中的流行度偏差可能会导致流行的项目优于个人偏好。本文提出了一种新的推荐框架，用于增强基于对比L收益（DECL）的流行度偏差的D is E tanglement。在 DECL 中，用户和项目的兴趣和一致性表示集是通过解耦表示学习过程生成的。然后执行对比学习过程以优化表示空间中解纠缠集的分布。设计定制的损失函数以方便参数优化，并根据兴趣和符合度做出最终推荐。在三个真实数据集上进行了大量的实验和比较研究，以验证所提出的 DECL 框架的有效性。实验结果表明，与state-of-the-art方法相比，DECL在Ciao数据集上可以实现高达10.69%的性能提升。这表明所提出的系统可以有效消除推荐中的流行度偏差，具有巨大的应用潜力。