In a collaborative-filtering recommendation scenario, biases in the data will likely propagate in the learned recommendations. In this paper we focus on the so-called mainstream bias: the tendency of a recommender system to provide better recommendations to users who have a mainstream taste, as opposed to non-mainstream users. We propose NAECF, a conceptually simple but effective idea to address this bias. The idea consists of adding an autoencoder (AE) layer when learning user and item representations with text-based Convolutional Neural Networks. The AEs, one for the users and one for the items, serve as adversaries to the process of minimizing the rating prediction error when learning how to recommend. They enforce that the specific unique properties of all users and items are sufficiently well incorporated and preserved in the learned representations. These representations, extracted as the bottlenecks of the corresponding AEs, are expected to be less biased towards mainstream users, and to provide more balanced recommendation utility across all users. Our experimental results confirm these expectations, significantly improving the recommendations for non-mainstream users while maintaining the recommendation quality for mainstream users. Our results emphasize the importance of deploying extensive content-based features, such as online reviews, in order to better represent users and items to maximize the de-biasing effect.
翻译:在合作过滤建议的设想中,数据中的偏差可能会在学到的建议中传播。在本文件中,我们侧重于所谓的主流偏差:推荐者系统倾向于向有主流口味的用户而不是非主流用户提供更好的建议,我们提出NAECF,这是一个概念上简单但有效的解决这种偏差的构想。这个构想包括在学习基于文本的神经神经网络的用户和项目演示时添加自动编码器(AE)层。AES,供用户使用,供项目使用,在学习如何推荐时,作为尽量减少评级预测错误的对手。他们强调所有用户和项目的特殊特性都充分纳入并保存在所学的表述中。这些表述作为相应的AE的瓶颈,预计对主流用户的偏向性较小,为所有用户提供更平衡的建议效用。我们的实验结果证实了这些期望,大大改进了对非主流用户的建议,同时保持了主流用户的建议质量。我们的结果强调,在应用内容基础的广泛特性方面,如在线审查等,必须最大限度地反映用户的特性。