Abstract
In recent years, the research of combining a knowledge graph with recommendation systems has caused widespread concern. By studying the interconnections in knowledge graphs, potential connections between users and items can be discovered, which provides abundant and complementary information for recommendation of items. However, most existing studies have not effectively established the relation between entities and users. Therefore, the recommendation results may be affected by some unrelated entities. In this paper, we propose a neighborhood aggregation collaborative filtering (NACF) based on knowledge graph. It uses the knowledge graph to spread and extract the user’s potential interest, and iteratively injects them into the user features with attentional deviation. We conducted a large number of experiments on three public datasets; we verifyied that NACF is ahead of the most advanced models in top-k recommendation and click-through rate (CTR) prediction.
Highlights
At present, many online recommendation services, such as e-commerce, advertising and social media, are based on historical interactions to estimate the user’s interest in the items
We propose a new neighborhood aggregation collaborative filtering based on a knowledge graph, which iteratively encodes the potential information of the knowledge graph into user features
The results show that neighborhood aggregation collaborative filtering (NACF) is significantly better than the existing methods in top-k recommendation and click-through rate (CTR) prediction
Summary
Many online recommendation services, such as e-commerce, advertising and social media, are based on historical interactions (purchases or clicks) to estimate the user’s interest in the items. Collaborative filtering (CF) personalizes recommendations by analyzing users with similar behaviors [1]. Jamali et al [2] proposed a random walk model combining the trust-based and collaborative filtering approach for recommendation. Jamali et al [4] proposed a social network recommendation method based on matrix decomposition. Traditional collaborative filtering cannot solve the cold start problem effectively. Researchers add some auxiliary information to solve such problems, such as social network [5], item’s attributes [6], images [7] and heterogeneous network [8]. Among the various auxiliary information, the knowledge graph (KG)
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