TRAL: A Tag-Aware Recommendation Algorithm Based on Attention Learning
A social tagging system improves recommendation performance by introducing tags as auxiliary information. These tags are text descriptions of target items provided by individual users, which can be arbitrary words or phrases, so they can provide more abundant information about user interests and ite...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/2/814 |
| _version_ | 1797446699774377984 |
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| author | Yi Zuo Shengzong Liu Yun Zhou Huanhua Liu |
| author_facet | Yi Zuo Shengzong Liu Yun Zhou Huanhua Liu |
| author_sort | Yi Zuo |
| collection | DOAJ |
| description | A social tagging system improves recommendation performance by introducing tags as auxiliary information. These tags are text descriptions of target items provided by individual users, which can be arbitrary words or phrases, so they can provide more abundant information about user interests and item characteristics. However, there are many problems to be solved in tag information, such as data sparsity, ambiguity, and redundancy. In addition, it is difficult to capture multi-aspect user interests and item characteristics from these tags, which is essential to the recommendation performance. In the view of these situations, we propose a tag-aware recommendation model based on attention learning, which can capture diverse tag-based potential features for users and items. The proposed model adopts the embedding method to produce dense tag-based feature vectors for each user and each item. To compress these vectors into a fixed-length feature vector, we construct an attention pooling layer that can automatically allocate different weights to different features according to their importance. We concatenate the feature vectors of users and items as the input of a multi-layer fully connected network to learn non-linear high-level interaction features. In addition, a generalized linear model is also conducted to extract low-level interaction features. By integrating these features of different types, the proposed model can provide more accurate recommendations. We establish extensive experiments on two real-world datasets to validate the effect of the proposed model. Comparable results show that our model perform better than several state-of-the-art tag-aware recommendation methods in terms of HR and NDCG metrics. Further ablation studies also demonstrate the effectiveness of attention learning. |
| first_indexed | 2024-03-09T13:45:20Z |
| format | Article |
| id | doaj.art-8ccc5cce868c48b586f4d27566700884 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T13:45:20Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8ccc5cce868c48b586f4d275667008842023-11-30T21:01:51ZengMDPI AGApplied Sciences2076-34172023-01-0113281410.3390/app13020814TRAL: A Tag-Aware Recommendation Algorithm Based on Attention LearningYi Zuo0Shengzong Liu1Yun Zhou2Huanhua Liu3School of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, ChinaSchool of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, ChinaSchool of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, ChinaSchool of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, ChinaA social tagging system improves recommendation performance by introducing tags as auxiliary information. These tags are text descriptions of target items provided by individual users, which can be arbitrary words or phrases, so they can provide more abundant information about user interests and item characteristics. However, there are many problems to be solved in tag information, such as data sparsity, ambiguity, and redundancy. In addition, it is difficult to capture multi-aspect user interests and item characteristics from these tags, which is essential to the recommendation performance. In the view of these situations, we propose a tag-aware recommendation model based on attention learning, which can capture diverse tag-based potential features for users and items. The proposed model adopts the embedding method to produce dense tag-based feature vectors for each user and each item. To compress these vectors into a fixed-length feature vector, we construct an attention pooling layer that can automatically allocate different weights to different features according to their importance. We concatenate the feature vectors of users and items as the input of a multi-layer fully connected network to learn non-linear high-level interaction features. In addition, a generalized linear model is also conducted to extract low-level interaction features. By integrating these features of different types, the proposed model can provide more accurate recommendations. We establish extensive experiments on two real-world datasets to validate the effect of the proposed model. Comparable results show that our model perform better than several state-of-the-art tag-aware recommendation methods in terms of HR and NDCG metrics. Further ablation studies also demonstrate the effectiveness of attention learning.https://www.mdpi.com/2076-3417/13/2/814attention learningtag informationtag-aware recommendation |
| spellingShingle | Yi Zuo Shengzong Liu Yun Zhou Huanhua Liu TRAL: A Tag-Aware Recommendation Algorithm Based on Attention Learning Applied Sciences attention learning tag information tag-aware recommendation |
| title | TRAL: A Tag-Aware Recommendation Algorithm Based on Attention Learning |
| title_full | TRAL: A Tag-Aware Recommendation Algorithm Based on Attention Learning |
| title_fullStr | TRAL: A Tag-Aware Recommendation Algorithm Based on Attention Learning |
| title_full_unstemmed | TRAL: A Tag-Aware Recommendation Algorithm Based on Attention Learning |
| title_short | TRAL: A Tag-Aware Recommendation Algorithm Based on Attention Learning |
| title_sort | tral a tag aware recommendation algorithm based on attention learning |
| topic | attention learning tag information tag-aware recommendation |
| url | https://www.mdpi.com/2076-3417/13/2/814 |
| work_keys_str_mv | AT yizuo tralatagawarerecommendationalgorithmbasedonattentionlearning AT shengzongliu tralatagawarerecommendationalgorithmbasedonattentionlearning AT yunzhou tralatagawarerecommendationalgorithmbasedonattentionlearning AT huanhualiu tralatagawarerecommendationalgorithmbasedonattentionlearning |