Dynamic Context in Graph Neural Networks for Item Recommendation

Asma Sattar, D. Bacciu
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Abstract

Graph neural networks allow to build recommendation systems which can straightforwardly take into account relational knowledge concerning multiple types of interactions, such as user-item relationships, but also interactions between users and within items. Graph-based approaches in the literature consider such interactions to be static, independent of the surroundings. In this paper, we put forward a novel approach to graph-based item recommendation built on the foundational idea that relational knowledge is characterized by a dynamic nature of the user and its surroundings. We claim that being able to capture such dynamic knowledge allows to build richer contexts upon which more precise recommendations can be built, e.g., taking into account current location, weather conditions, and user mood. The paper provides recipes to build and integrate dynamic user and item contexts in existing item recommendation tasks. We also introduce a novel Dynamic Context-aware Graph Neural Network (DCGNN) that can effectively leverage the knowledge of surroundings to learn the context-aware recommendation behaviour of users. The empirical analysis shows how our model outperforms static state-of-the-art approaches on four movie and travel recommendation benchmarks.
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面向项目推荐的图神经网络动态上下文
图神经网络允许构建推荐系统,该系统可以直接考虑涉及多种交互类型的关系知识,例如用户-物品关系,以及用户之间和物品内部的交互。文献中基于图形的方法认为这种相互作用是静态的,独立于周围环境。在本文中,我们提出了一种新的基于图的商品推荐方法,该方法建立在关系知识以用户及其周围环境的动态特性为特征的基本思想之上。我们声称,能够捕获这种动态知识可以构建更丰富的上下文,在此基础上可以构建更精确的推荐,例如,考虑当前位置、天气条件和用户情绪。本文提供了在现有的项目推荐任务中构建和集成动态用户和项目上下文的方法。我们还引入了一种新的动态上下文感知图神经网络(DCGNN),它可以有效地利用周围环境的知识来学习用户的上下文感知推荐行为。实证分析表明,我们的模型在四个电影和旅游推荐基准上优于静态的最先进的方法。
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