Abstract
In recent years, many researchers have devoted time to designing algorithms used to introduce external information from knowledge graphs, to solve the problems of data sparseness and the cold start, and thus improve the performance of recommendation systems. Inspired by these studies, we proposed KANR, a knowledge graph-enhanced attention aggregation network for making recommendations. This is an end-to-end deep learning model using knowledge graph embedding to enhance the attention aggregation network for making recommendations. It consists of three main parts. The first is the attention aggregation network, which collect the user’s interaction history and captures the user’s preference for each item. The second is the knowledge graph-embedded model, which aims to integrate the knowledge. The semantic information of the nodes and edges in the graph is mapped to the low-dimensional vector space. The final part is the information interaction unit, which is used for fusing the features of two vectors. Experiments showed that our model achieved a stable improvement compared to the baseline model in making recommendations for movies, books, and music.
Highlights
In many online services such as e-commerce, Internet advertising, and social media, people access online content, generating a large number of interactive records
In the recommendation task based on the knowledge graph, users and items correspond to nodes in the graph, and the relationships between items correspond to edges in the graph
In the research of recommendation systems based on the knowledge graph, the entities are regarded as items and the relationship between the entities is equivalent to the attributes between items
Summary
In many online services such as e-commerce, Internet advertising, and social media, people access online content (usually in the form of purchases or clicks), generating a large number of interactive records. To reduce the impact of information overload, researchers have proposed recommendation systems to satisfy the personalized needs of users. Traditional recommendation methods, such as collaborative filtering (CF) and matrix factorization (MF) [1], predict whether users are interested in an item based on their historical interactions. These methods usually suffer from the problems of data sparsity and the cold start. Researchers have introduced external information to solve these problems; for example, social networks [2,3], item attributes [4], knowledge graphs [5], and other heterogeneous networks. Many research institutions open-source their academic knowledge graphs, such as DBpedia [6] and Google Knowledge Graph [7]
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