Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism
Heterogeneous information networks (HINs) contain a rich network structure and semantic information, which makes them commonly used in recommendation systems. However, most of the existing HIN-based recommendation systems rely on meta-paths for information extraction, lack meta-path information supp...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/1/353 |
| _version_ | 1797359084144427008 |
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| author | Li Li Xiangquan Gui Rui Lv |
| author_facet | Li Li Xiangquan Gui Rui Lv |
| author_sort | Li Li |
| collection | DOAJ |
| description | Heterogeneous information networks (HINs) contain a rich network structure and semantic information, which makes them commonly used in recommendation systems. However, most of the existing HIN-based recommendation systems rely on meta-paths for information extraction, lack meta-path information supplements, and rarely learn complex structure information in heterogeneous graphs. To address these issues, we develop a novel recommendation algorithm that integrates the attention mechanism, meta-paths, and neighbor node information (AMNRec). In the heterogeneous information network, the missing information of the meta-path is supplemented by extracting the information of users and items’ neighbor nodes. The rich interactions between nodes are captured through convolution, and the embedded representation of nodes and meta-paths is obtained through the attention mechanism. TOP-N recommendation is completed by combining users, items, neighbor nodes, and meta-paths. Experiments on three public datasets show that AMNRec not only has the best recommendation performance but also has good interpretability of the recommendation results compared with the six recommendation benchmark algorithms. |
| first_indexed | 2024-03-08T15:11:43Z |
| format | Article |
| id | doaj.art-8b5bd134ab3f4b8ab504f766d79de648 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T15:11:43Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8b5bd134ab3f4b8ab504f766d79de6482024-01-10T14:51:50ZengMDPI AGApplied Sciences2076-34172023-12-0114135310.3390/app14010353Recommendation Algorithm Based on Heterogeneous Information Network and Attention MechanismLi Li0Xiangquan Gui1Rui Lv2School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, ChinaSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, ChinaDepartment of Management, Lanzhou Institute of Technology, Lanzhou 730050, ChinaHeterogeneous information networks (HINs) contain a rich network structure and semantic information, which makes them commonly used in recommendation systems. However, most of the existing HIN-based recommendation systems rely on meta-paths for information extraction, lack meta-path information supplements, and rarely learn complex structure information in heterogeneous graphs. To address these issues, we develop a novel recommendation algorithm that integrates the attention mechanism, meta-paths, and neighbor node information (AMNRec). In the heterogeneous information network, the missing information of the meta-path is supplemented by extracting the information of users and items’ neighbor nodes. The rich interactions between nodes are captured through convolution, and the embedded representation of nodes and meta-paths is obtained through the attention mechanism. TOP-N recommendation is completed by combining users, items, neighbor nodes, and meta-paths. Experiments on three public datasets show that AMNRec not only has the best recommendation performance but also has good interpretability of the recommendation results compared with the six recommendation benchmark algorithms.https://www.mdpi.com/2076-3417/14/1/353heterogeneous information networkmeta-pathneighbor informationattention mechanismrecommendation systemconvolutional neural network |
| spellingShingle | Li Li Xiangquan Gui Rui Lv Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism Applied Sciences heterogeneous information network meta-path neighbor information attention mechanism recommendation system convolutional neural network |
| title | Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism |
| title_full | Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism |
| title_fullStr | Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism |
| title_full_unstemmed | Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism |
| title_short | Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism |
| title_sort | recommendation algorithm based on heterogeneous information network and attention mechanism |
| topic | heterogeneous information network meta-path neighbor information attention mechanism recommendation system convolutional neural network |
| url | https://www.mdpi.com/2076-3417/14/1/353 |
| work_keys_str_mv | AT lili recommendationalgorithmbasedonheterogeneousinformationnetworkandattentionmechanism AT xiangquangui recommendationalgorithmbasedonheterogeneousinformationnetworkandattentionmechanism AT ruilv recommendationalgorithmbasedonheterogeneousinformationnetworkandattentionmechanism |