面向个性化推荐的 node2vec-side 融合知识表示.

The knowledge graph in the recommendation system plays a vital role in the recommendation effect of the system, and the knowledge representation in the graph becomes a key factor affecting the recommendation system, which has become one of the current research hotspots. This paper proposed a node2vec-based knowledge representation node2 vec-side based on the traditional node2vec model by adding relational representation and diversifing wandering strategy to the structural characteristics of the knowledge graph in recommendation system, which combined with the knowledge graph network structure of recommendation system to explore the potential association relationship between nodes of large-scale recommendation entities, reduced the complexity of the representation and improved interpretability. After time complexity analysis, it could be seen that the proposed knowledge representation is lower than Trans series and RGCN in terms of complexity. Link prediction experiments were conducted on the traditional knowledge graph datasets FB15K, WN18, and recommendation domain datasets MovieLens-1M, Book-Crossing, Last. FM respectively. The experimental results show that on the MovieLens-1M dataset, hits @ 10 improves 5.5%12.1% and MRR improves 0. 090. 24, respectively. On the Book-Crossing dataset, hits @ 10 improves 3.5%-20.6%, and MRR improves 0.04-0.24 on average, respectively. And on the Last. FM dataset, hits@1 improves 0.3%-8.5% and MRR improves 0.04-0. 16 on average. It is better than the existing algorithms and verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]