序列推荐的分类感知多跳推理网络

Jin Huang, Z. Ren, Wayne Xin Zhao, Gaole He, Ji-Rong Wen, Daxiang Dong
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引用次数: 67

摘要

在本文中,我们主要研究使用分类法数据的顺序推荐任务。现有的序列推荐方法通常采用单一的矢量化表示来学习整体的序列特征,并且在捕获上下文信息上的多粒度序列特征方面建模能力有限。此外,现有方法往往直接将上下文信息衍生的特征向量作为辅助输入,难以充分利用上下文信息中的结构模式来学习偏好表示。为了解决上述问题,我们提出了一种新的分类感知多跳推理网络,称为TMRN,它将基于gru的基本顺序推荐与精心设计的基于内存的多跳推理体系结构集成在一起。为了提高模型的推理能力,我们将分类数据作为结构知识来指导模型的学习。我们将顺序推荐中的用户偏好学习与分类法中的类别层次结构联系起来。给定一个用户,对于每个推荐,我们根据他/她的总体顺序偏好学习一个唯一的偏好表示,对应于分类法中的每个级别。通过这种方式,总体的粗粒度偏好表示可以在从一般到特定的不同级别上逐渐细化,并且我们能够捕获分类法上用户偏好的演变和细化,这使得我们的模型具有高度的可解释性。大量的实验表明,我们提出的模型在有效性和可解释性方面优于最先进的基线。
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Taxonomy-Aware Multi-Hop Reasoning Networks for Sequential Recommendation
In this paper, we focus on the task of sequential recommendation using taxonomy data. Existing sequential recommendation methods usually adopt a single vectorized representation for learning the overall sequential characteristics, and have a limited modeling capacity in capturing multi-grained sequential characteristics over context information. Besides, existing methods often directly take the feature vectors derived from context information as auxiliary input, which is difficult to fully exploit the structural patterns in context information for learning preference representations. To address above issues, we propose a novel Taxonomy-aware Multi-hop Reasoning Network, named TMRN, which integrates a basic GRU-based sequential recommender with an elaborately designed memory-based multi-hop reasoning architecture. For enhancing the reasoning capacity, we incorporate taxonomy data as structural knowledge to instruct the learning of our model. We associate the learning of user preference in sequential recommendation with the category hierarchy in the taxonomy. Given a user, for each recommendation, we learn a unique preference representation corresponding to each level in the taxonomy based on her/his overall sequential preference. In this way, the overall, coarse-grained preference representation can be gradually refined in different levels from general to specific, and we are able to capture the evolvement and refinement of user preference over the taxonomy, which makes our model highly explainable. Extensive experiments show that our proposed model is superior to state-of-the-art baselines in terms of both effectiveness and interpretability.
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