Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception

TL;DR

This paper introduces KGUPN, an end-to-end knowledge graph-based framework that incorporates user perception for improved scientific and technological news recommendation, outperforming existing methods on real datasets.

Contribution

The paper proposes KGUPN, a novel framework integrating knowledge graph and user awareness into news recommendation, addressing limitations of prior embedding and path-based approaches.

Findings

KGUPN significantly outperforms state-of-the-art baselines.

The framework effectively captures user perception and contextual information.

Experimental results validate the superiority of KGUPN on real datasets.

Abstract

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.