基于异质信息网络表示学习与注意力神经网络的推荐算法

doi:10.11896/jsjkx.200800226

Abstract

Abstract: Recommendation system,as a very effective technique to solve the information overload,has received a great deal of attention from researchers.However,the real application of recommending systems can be modeled as heterogeneous networks with multi-typed nodes and relations.Thus,heterogeneous network embedding based recommendation becomes a very hot research topic in recent years.However,most of the existing studies do not fully explore the auxiliary information and complex relations which are valuable for enhancing recommending performance.To address the above problems,a recommendation algorithm based on heterogeneous information network embedding and attention neural network is proposed.First,this paper proposes a heterogeneous information network embedding method that maintains semantic relationship and topological structure simultaneously.Then,it designs a meta-path based random walk strategy to extract node sequences from heterogeneous information networks.All the sequences are filtered and then employed to learn the embeddings for each user and item in different meta-paths.At last,this paper presents a recommendation algorithm based on attention neural network with the above embeddings as input.The attention network composed of attention layers and hidden layers is able to explore the complex relationships and hence enhance the performance of recommendation.To verify the effectiveness of the proposed method,this paper conducts experiments on two kinds of real-world datasets and makes a comparison with three competitive algorithms.The results show that the proposed algorithm improves the recommending performance in terms of MAE and RMSE,with a maximum increase of 8.9%.

Key words: Attention neural network, Heterogeneous information networks, Meta-path, Recommendation algorithm, Representation learning

CLC Number:

TP181

ZHAO Jin-long, ZHAO Zhong-ying. Recommendation Algorithm Based on Heterogeneous Information Network Embedding and Attention Neural Network[J].Computer Science, 2021, 48(8): 72-79.

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