The goal of session-based recommendation (SR) models is to utilize the information from past actions (e.g. item/product clicks) in a session to recommend items that a user is likely to click next. Recently it has been shown that the sequence of item interactions in a session can be modeled as graph-structured data to better account for complex item transitions. Graph neural networks (GNNs) can learn useful representations for such session-graphs, and have been shown to improve over sequential models such as recurrent neural networks [14]. However, we note that these GNN-based recommendation models suffer from popularity bias: the models are biased towards recommending popular items, and fail to recommend relevant long-tail items (less popular or less frequent items). Therefore, these models perform poorly for the less popular new items arriving daily in a practical online setting. We demonstrate that this issue is, in part, related to the magnitude or norm of the learned item and session-graph representations (embedding vectors). We propose a training procedure that mitigates this issue by using normalized representations. The models using normalized item and session-graph representations perform significantly better: i. for the less popular long-tail items in the offline setting, and ii. for the less popular newly introduced items in the online setting. Furthermore, our approach significantly improves upon existing state-of-the-art on three benchmark datasets.
翻译:届会建议(SR)模式的目标是利用届会中以往行动(如项目/产品点击)中的信息,建议用户可能下一轮点击的项目。最近显示,届会中项目互动的顺序可以模拟成图表结构数据,以更好地说明复杂的项目过渡情况。图表神经网络(GNNs)可以为此类届会了解有用的表述方式,并表明在诸如经常性神经网络等相继模式(14)]的基础上改进了。然而,我们注意到,这些基于GNN的建议模式受到流行偏见的影响:这些模式偏向于推荐受欢迎的项目,而没有推荐相关的长尾项目(不太受欢迎或不太频繁的项目)。因此,这些模式对于在实际在线环境下每天抵达的不太受欢迎的新项目来说表现不佳。我们证明,这一问题部分与所学项目的规模或规范以及届会表述(含病媒)等相近。我们建议采用一种培训程序,通过使用标准化的表述方式来缓解这一问题。使用标准化项目和届会表述方式的模式对推荐的长尾项目(不太受欢迎的项目)没有推荐,也没有推荐推荐,因此,这些模式在网上对不受欢迎的项目进行大幅改进。