Multi-Channel Hypergraph Collaborative Filtering with Attribute Inference

Author:

Jiang Yutong1,Gao Yuhan23,Sun Yaoqi23,Wang Shuai23,Yan Chenggang2

Affiliation:

1. School of Mechanical, Electrical, and Information Engineering, Shandong University, Weihai 264209, China

2. Department of Automation, Hangzhou Dianzi University, Hangzhou 310018, China

3. Lishui Institute of Hangzhou Dianzi University, Lishui 323000, China

Abstract

In the field of collaborative filtering, attribute information is often integrated to improve recommendations. However, challenges remain unaddressed. Firstly, existing data modeling methods often fall short of appropriately handling attribute information. Secondly, attribute data are often sparse and can potentially impact recommendation performance due to the challenge of incomplete correspondence between the attribute information and the recommendations. To tackle these challenges, we propose a hypergraph collaborative filtering with attribute inference (HCFA) framework, which segregates attribute and user behavior information into distinct channels and leverages hypergraphs to capture high-order correlations among vertices, offering a more natural approach to modeling. Furthermore, we introduce behavior-based attribute confidence (BAC) for assessing the reliability of inferred attributes concerning the corresponding behaviors and update the most credible portions to enhance recommendation quality. Extensive experiments conducted on three public benchmarks demonstrate the superiority of our model. It consistently outperforms other state-of-the-art approaches, with ablation experiments further confirming the effectiveness of our proposed method.

Funder

National Nature Science Foundation of China

Zhejiang Provincial Natural Science Foundation of China

“Pioneer” and “Leading Goose” R&D Program of Zhejiang Province

Publisher

MDPI AG

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