Multi-Channel Hypergraph Collaborative Filtering with Attribute Inference
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 a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/13/5/903 |
| _version_ | 1797264627471482880 |
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| author | Yutong Jiang Yuhan Gao Yaoqi Sun Shuai Wang Chenggang Yan |
| author_facet | Yutong Jiang Yuhan Gao Yaoqi Sun Shuai Wang Chenggang Yan |
| author_sort | Yutong Jiang |
| collection | DOAJ |
| description | 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 <b>h</b>ypergraph <b>c</b>ollaborative <b>f</b>iltering with <b>a</b>ttribute 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 <b>b</b>ehavior-based <b>a</b>ttribute <b>c</b>onfidence (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. |
| first_indexed | 2024-04-25T00:31:54Z |
| format | Article |
| id | doaj.art-250491ba89174ca0ad5f9c2286edc401 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-25T00:31:54Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-250491ba89174ca0ad5f9c2286edc4012024-03-12T16:42:32ZengMDPI AGElectronics2079-92922024-02-0113590310.3390/electronics13050903Multi-Channel Hypergraph Collaborative Filtering with Attribute InferenceYutong Jiang0Yuhan Gao1Yaoqi Sun2Shuai Wang3Chenggang Yan4School of Mechanical, Electrical, and Information Engineering, Shandong University, Weihai 264209, ChinaDepartment of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaDepartment of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaDepartment of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaDepartment of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaIn 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 <b>h</b>ypergraph <b>c</b>ollaborative <b>f</b>iltering with <b>a</b>ttribute 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 <b>b</b>ehavior-based <b>a</b>ttribute <b>c</b>onfidence (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.https://www.mdpi.com/2079-9292/13/5/903hypergraph learningcollaborative filteringattribute inference |
| spellingShingle | Yutong Jiang Yuhan Gao Yaoqi Sun Shuai Wang Chenggang Yan Multi-Channel Hypergraph Collaborative Filtering with Attribute Inference Electronics hypergraph learning collaborative filtering attribute inference |
| title | Multi-Channel Hypergraph Collaborative Filtering with Attribute Inference |
| title_full | Multi-Channel Hypergraph Collaborative Filtering with Attribute Inference |
| title_fullStr | Multi-Channel Hypergraph Collaborative Filtering with Attribute Inference |
| title_full_unstemmed | Multi-Channel Hypergraph Collaborative Filtering with Attribute Inference |
| title_short | Multi-Channel Hypergraph Collaborative Filtering with Attribute Inference |
| title_sort | multi channel hypergraph collaborative filtering with attribute inference |
| topic | hypergraph learning collaborative filtering attribute inference |
| url | https://www.mdpi.com/2079-9292/13/5/903 |
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