基于知识图的确定性点过程增强推荐多样性

Lu Gan, Diana Nurbakova, Léa Laporte, S. Calabretto
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引用次数: 26

摘要

Top-N推荐广泛应用于现实生活的各个领域,由于可获得的多类型信息、人工智能模型的新进展以及对用户满意度的深入理解,Top-N推荐不断引起研究人员和业界的高度关注。虽然在过去的几十年里,准确性一直是推荐问题的主要问题,但问题的其他方面,即多样性和可解释性,受到的关注要少得多。在本文中,我们的重点是增强top-N推荐的多样性,同时确保准确性和多样性之间的权衡。因此,我们提出了一个利用知识图嵌入和确定点过程(DPP)的有效框架DivKG。首先,我们通过知识图结构捕获用户、项目和附加实体之间的不同类型的关系。然后,我们通过优化具有各种历史交互的基于边际的损失,将实体和关系表示为k维向量。我们使用这些表示来构造DPP的核矩阵,以便做出top-N的多样化预测。我们在MovieLens数据集和IMDb数据集上评估我们的框架。我们的实证结果显示,在准确性和多样性指标方面,比最先进的技术有了实质性的改进。
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Enhancing Recommendation Diversity using Determinantal Point Processes on Knowledge Graphs
Top-N recommendations are widely applied in various real life domains and keep attracting intense attention from researchers and industry due to available multi-type information, new advances in AI models and deeper understanding of user satisfaction. Whileaccuracy has been the prevailing issue of the recommendation problem for the last decades, other facets of the problem, namelydiversity andexplainability, have received much less attention. In this paper, we focus on enhancing diversity of top-N recommendation, while ensuring the trade-off between accuracy and diversity. Thus, we propose an effective framework DivKG leveraging knowledge graph embedding and determinantal point processes (DPP). First, we capture different kinds of relations among users, items and additional entities through a knowledge graph structure. Then, we represent both entities and relations as k-dimensional vectors by optimizing a margin-based loss with all kinds of historical interactions. We use these representations to construct kernel matrices of DPP in order to make top-N diversified predictions. We evaluate our framework on MovieLens datasets coupled with IMDb dataset. Our empirical results show substantial improvement over the state-of-the-art regarding both accuracy and diversity metrics.
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