Heterogeneous Information Network-Based Recommendation with Metapath Search and Memory Network Architecture Search

Peisen Yuan (), Yi Sun and Hengliang Wang ()
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Peisen Yuan: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Yi Sun: Research Institute of Intelligent Complex System, Fudan University, Shanghai 200433, China
Hengliang Wang: Laboratory for Data and Virtual, ShangHai BNC, Shanghai 200336, China

Mathematics, 2022, vol. 10, issue 16, 1-18

Abstract: Recommendation systems are now widely used on the Internet. In recommendation systems, user preferences are predicted by the interaction of users with products, such as clicks or purchases. Usually, the heterogeneous information network is used to capture heterogeneous semantic information in data, which can be used to solve the sparsity problem and the cold-start problem. In a more complex heterogeneous information network, the types of nodes and edges are very large, so there are lots of types of metagraphs in a complex heterogeneous information network. At the same time, machine learning tasks on heterogeneous information networks have a large number of parameters and neural network architectures that need to be set artificially. The main goal is to find the optimal hyperparameter settings and neural network architectures for the performance of a task in the set of hyperparameter space. To address this problem, we propose a metapath search method for heterogeneous information networks based on a network architecture search, which can search for metapaths that are more suitable for different heterogeneous information networks and recommendation tasks. We conducted experiments on Amazon and Yelp datasets and compared the architecture settings obtained from an automatic search with manually set structures to verify the effectiveness of the algorithm.

Keywords: heterogeneous information network; recommender system; network architecture search; graph neural networks; metagraph; network embedding (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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