Graph-Based Feature Crossing to Enhance Recommender Systems

Congyu Cai, Hong Chen, Yunxuan Liu, Daoquan Chen, Xiuze Zhou () and Yuanguo Lin ()
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Congyu Cai: School of Marxism, Jimei University, Xiamen 361021, China
Hong Chen: Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China
Yunxuan Liu: School of Computer Engineering, Jimei University, Xiamen 361021, China
Daoquan Chen: School of Intelligent Transportation, Zhejiang Polytechnic University of Mechanical and Electrical Engineering, Hangzhou 310053, China
Xiuze Zhou: Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China
Yuanguo Lin: School of Computer Engineering, Jimei University, Xiamen 361021, China

Mathematics, 2025, vol. 13, issue 2, 1-17

Abstract: In recommendation tasks, most existing models that learn users’ preferences from user–item interactions ignore the relationships between items. Additionally, ensuring that the crossed features capture both global graph structures and local context is non-trivial, requiring innovative techniques for multi-scale representation learning. To overcome these difficulties, we develop a novel neural network, CoGraph, which uses a graph to build the relations between items. The item co-occurrence pattern assumes that certain items consistently appear in pairs in users’ viewing or consumption logs. First, to learn relationships between items, a graph whose distance is measured by Normalised Point-Wise Mutual Information (NPMI) is applied to link items for the co-occurrence pattern. Then, to learn as many useful features as possible for higher recommendation quality, a Convolutional Neural Network (CNN) and the Transformer model are used to parallelly learn local and global feature interactions. Finally, a series of comprehensive experiments were conducted on several public data sets to show the performance of our model. It provides valuable insights into the capability of our model in recommendation tasks and offers a viable pathway for the public data operation.

Keywords: recommender systems; convolutional neural network; graph neural network; co-occurrence pattern; transformer model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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