Multi-Behavior Enhanced Heterogeneous Graph Convolutional Networks Recommendation Algorithm based on Feature-Interaction
Graph convolution neural networks have shown powerful ability in recommendation, thanks to extracting the user-item collaboration signal from users' historical interaction information. However, many existing studies often learn the final embedded representation of items and users through IDs of...
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| Veröffentlicht in: | Applied artificial intelligence Jg. 37; H. 1 |
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| Sprache: | Englisch |
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Taylor & Francis
31.12.2023
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| Abstract | Graph convolution neural networks have shown powerful ability in recommendation, thanks to extracting the user-item collaboration signal from users' historical interaction information. However, many existing studies often learn the final embedded representation of items and users through IDs of user and item, which cannot make well explanation why user choose the item. By making good use of item's attribute, the networks will gain better interpretability. In this article, we construct a heterogeneous tripartite graph consisting of user-item-feature, and propose the attention interaction graph convolutional neural network recommendation algorithm (ATGCN). We embed multi-feature fusion of users and items into the user feature interaction layer by using multi-head-attention, which explore the user's potential preference to update the user's embedded representation. Through the neighborhood aggregation of graph convolution, the feature neighbors' aggregation of items is constructed to achieve higher-order feature fusions, and the neighborhood aggregation of users and items is carried out on the historical interaction information. Then, the final embedding vector representations of user and item are obtained after many iterations. We verify the effectiveness of our proposed method on three publicly available datasets and ATGCN has improved 1.59%, 2.03%, and 1.27% in normalized discounted cumulative gain (NDCG), Precision and Recall, respectively. |
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| AbstractList | Graph convolution neural networks have shown powerful ability in recommendation, thanks to extracting the user-item collaboration signal from users’ historical interaction information. However, many existing studies often learn the final embedded representation of items and users through IDs of user and item, which cannot make well explanation why user choose the item. By making good use of item’s attribute, the networks will gain better interpretability. In this article, we construct a heterogeneous tripartite graph consisting of user-item-feature, and propose the attention interaction graph convolutional neural network recommendation algorithm (ATGCN). We embed multi-feature fusion of users and items into the user feature interaction layer by using multi-head-attention, which explore the user’s potential preference to update the user’s embedded representation. Through the neighborhood aggregation of graph convolution, the feature neighbors’ aggregation of items is constructed to achieve higher-order feature fusions, and the neighborhood aggregation of users and items is carried out on the historical interaction information. Then, the final embedding vector representations of user and item are obtained after many iterations. We verify the effectiveness of our proposed method on three publicly available datasets and ATGCN has improved 1.59%, 2.03%, and 1.27% in normalized discounted cumulative gain (NDCG), Precision and Recall, respectively. |
| Author | Zhao, Fangtao Chen, Zheng Li, Yang Ma, Li Fu, Yingxun |
| Author_xml | – sequence: 1 givenname: Yang surname: Li fullname: Li, Yang organization: North China University of Technology – sequence: 2 givenname: Fangtao surname: Zhao fullname: Zhao, Fangtao organization: North China University of Technology – sequence: 3 givenname: Zheng surname: Chen fullname: Chen, Zheng organization: North China University of Technology – sequence: 4 givenname: Yingxun surname: Fu fullname: Fu, Yingxun organization: North China University of Technology – sequence: 5 givenname: Li surname: Ma fullname: Ma, Li email: mali@ncut.edu.cn organization: North China University of Technology |
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| Cites_doi | 10.48550/arXiv.1706.02263 10.1145/3501815 10.1016/j.knosys.2022.109185 10.1016/j.future.2021.06.007 10.1145/3565575 10.1007/978-3-031-15937-4_9 10.1145/3437963.3441746 10.1145/2827872 10.1145/3560487 10.1145/3397271.3401072 10.1145/3535101 10.1145/3292500.3330673 10.1145/3397271.3401063 10.1145/3308558.3313705 10.1109/TKDE.2020.3040772 10.1145/3292500.3330989 10.48550/arXiv.2109.11898 10.1145/3394486.3403373 10.48550/arXiv.1205.2618 10.1145/3292500.3330961 10.1109/TKDE.2020.3033673 10.48550/arXiv.1412.6980 10.1007/978-0-387-85820-3_1 10.1007/978-3-030-67664-3_21 10.1145/3038912.3052569 10.1145/3397271.3401123 10.1109/ICDE48307.2020.00019 |
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| Snippet | Graph convolution neural networks have shown powerful ability in recommendation, thanks to extracting the user-item collaboration signal from users' historical... Graph convolution neural networks have shown powerful ability in recommendation, thanks to extracting the user-item collaboration signal from users’ historical... |
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| Title | Multi-Behavior Enhanced Heterogeneous Graph Convolutional Networks Recommendation Algorithm based on Feature-Interaction |
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