论文标题
基于知识图的科学和技术新闻推荐用户感知
Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception
论文作者
论文摘要
现有的研究通常利用辅助信息，例如社交网络或项目属性来提高基于协作过滤的建议系统的性能。在本文中，使用用户感知的知识图用于获取侧面信息的来源。我们提出了KGUPN，以解决现有的基于嵌入的基于嵌入的知识图形感知建议方法的局限性，该方法将知识图和用户意识集成到科学和技术新闻推荐系统中。 KGUPN包含三个主要层，它们是传播表示层，上下文信息层和协作关系层。传播表示层通过在知识图中递归传播其邻居（可以是用户，新闻或关系）来改善实体的表示。上下文信息层通过编码新闻中出现的实体的行为信息来改善实体的表示。协作关系层补充了新闻知识图中实体之间的关系。现实世界数据集的实验结果表明，在科学和技术新闻推荐中，KGUPN明显优于最先进的基准。
Existing research usually utilizes side information such as social network or item attributes to improve the performance of collaborative filtering-based recommender systems. In this paper, the knowledge graph with user perception is used to acquire the source of side information. We proposed KGUPN to address the limitations of existing embedding-based and path-based knowledge graph-aware recommendation methods, an end-to-end framework that integrates knowledge graph and user awareness into scientific and technological news recommendation systems. KGUPN contains three main layers, which are the propagation representation layer, the contextual information layer and collaborative relation layer. The propagation representation layer improves the representation of an entity by recursively propagating embeddings from its neighbors (which can be users, news, or relationships) in the knowledge graph. The contextual information layer improves the representation of entities by encoding the behavioral information of entities appearing in the news. The collaborative relation layer complements the relationship between entities in the news knowledge graph. Experimental results on real-world datasets show that KGUPN significantly outperforms state-of-the-art baselines in scientific and technological news recommendation.
