An Efficient Recommendation Algorithm Based on Heterogeneous Information Network
Heterogeneous information networks can naturally simulate complex objects, and they can enrich recommendation systems according to the connections between different types of objects. At present, a large number of recommendation algorithms based on heterogeneous information networks have been propose...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6689323 |
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| _version_ | 1850211306024468480 |
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| author | Ying Yin Wanning Zheng |
| author_facet | Ying Yin Wanning Zheng |
| author_sort | Ying Yin |
| collection | DOAJ |
| description | Heterogeneous information networks can naturally simulate complex objects, and they can enrich recommendation systems according to the connections between different types of objects. At present, a large number of recommendation algorithms based on heterogeneous information networks have been proposed. However, the existing algorithms cannot extract and combine the structural features in heterogeneous information networks. Therefore, this paper proposes an efficient recommendation algorithm based on heterogeneous information network, which uses the characteristics of graph convolution neural network to automatically learn node information to extract heterogeneous information and avoid errors caused by the manual search for metapaths. Furthermore, by fully considering the scoring relationship between nodes, a calculation strategy combining heterogeneous information and a scoring information fusion strategy is proposed to solve the scoring between nodes, which makes the prediction scoring more accurate. Finally, by updating the nodes, the training scale is reduced, and the calculation efficiency is improved. The study conducted a large number of experiments on three real data sets with millions of edges. The results of the experiments show that compared with PMF, SemRec, and other algorithms, the proposed algorithm improves the recommendation accuracy MAE by approximately 3% and the RMSE by approximately 8% and reduces the time consumption significantly. |
| format | Article |
| id | doaj-art-8d58f081cf154dfaaf6db7ed11cb0736 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8d58f081cf154dfaaf6db7ed11cb07362025-08-20T02:09:35ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66893236689323An Efficient Recommendation Algorithm Based on Heterogeneous Information NetworkYing Yin0Wanning Zheng1School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning 110169, ChinaSchool of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning 110169, ChinaHeterogeneous information networks can naturally simulate complex objects, and they can enrich recommendation systems according to the connections between different types of objects. At present, a large number of recommendation algorithms based on heterogeneous information networks have been proposed. However, the existing algorithms cannot extract and combine the structural features in heterogeneous information networks. Therefore, this paper proposes an efficient recommendation algorithm based on heterogeneous information network, which uses the characteristics of graph convolution neural network to automatically learn node information to extract heterogeneous information and avoid errors caused by the manual search for metapaths. Furthermore, by fully considering the scoring relationship between nodes, a calculation strategy combining heterogeneous information and a scoring information fusion strategy is proposed to solve the scoring between nodes, which makes the prediction scoring more accurate. Finally, by updating the nodes, the training scale is reduced, and the calculation efficiency is improved. The study conducted a large number of experiments on three real data sets with millions of edges. The results of the experiments show that compared with PMF, SemRec, and other algorithms, the proposed algorithm improves the recommendation accuracy MAE by approximately 3% and the RMSE by approximately 8% and reduces the time consumption significantly.http://dx.doi.org/10.1155/2021/6689323 |
| spellingShingle | Ying Yin Wanning Zheng An Efficient Recommendation Algorithm Based on Heterogeneous Information Network Complexity |
| title | An Efficient Recommendation Algorithm Based on Heterogeneous Information Network |
| title_full | An Efficient Recommendation Algorithm Based on Heterogeneous Information Network |
| title_fullStr | An Efficient Recommendation Algorithm Based on Heterogeneous Information Network |
| title_full_unstemmed | An Efficient Recommendation Algorithm Based on Heterogeneous Information Network |
| title_short | An Efficient Recommendation Algorithm Based on Heterogeneous Information Network |
| title_sort | efficient recommendation algorithm based on heterogeneous information network |
| url | http://dx.doi.org/10.1155/2021/6689323 |
| work_keys_str_mv | AT yingyin anefficientrecommendationalgorithmbasedonheterogeneousinformationnetwork AT wanningzheng anefficientrecommendationalgorithmbasedonheterogeneousinformationnetwork AT yingyin efficientrecommendationalgorithmbasedonheterogeneousinformationnetwork AT wanningzheng efficientrecommendationalgorithmbasedonheterogeneousinformationnetwork |