Ebbinghaus forgetting curve and attention mechanism based recommendation algorithm
Traditional attention-based recommendation algorithms only use position embeddings to model user behavior sequences, however, ignore specific timestamp information, resulting in poor recommendation performance and overfitting of model training.The multi-task matrix factorization recommendation model...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2022-10-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022266/ |
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| _version_ | 1841530795602214912 |
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| author | Nan JIN Ruiqin WANG Yuecong LU |
| author_facet | Nan JIN Ruiqin WANG Yuecong LU |
| author_sort | Nan JIN |
| collection | DOAJ |
| description | Traditional attention-based recommendation algorithms only use position embeddings to model user behavior sequences, however, ignore specific timestamp information, resulting in poor recommendation performance and overfitting of model training.The multi-task matrix factorization recommendation model based on time attention was proposed, which used the attention mechanism to extract the neighborhood information for the user and item embedding, and used the Ebbinghaus forgetting curve to describe the changing characteristics of user interests over time.The model training process introduced a reinforcement learning strategy of experience replay to simulate the human memory review process.Experimental results on real datasets show that the proposed model has better recommendation performance than existing recommendation models. |
| format | Article |
| id | doaj-art-f16479880fc24987aebf0519a82528c6 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2022-10-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-f16479880fc24987aebf0519a82528c62025-01-15T03:00:00ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012022-10-0138899759576233Ebbinghaus forgetting curve and attention mechanism based recommendation algorithmNan JINRuiqin WANGYuecong LUTraditional attention-based recommendation algorithms only use position embeddings to model user behavior sequences, however, ignore specific timestamp information, resulting in poor recommendation performance and overfitting of model training.The multi-task matrix factorization recommendation model based on time attention was proposed, which used the attention mechanism to extract the neighborhood information for the user and item embedding, and used the Ebbinghaus forgetting curve to describe the changing characteristics of user interests over time.The model training process introduced a reinforcement learning strategy of experience replay to simulate the human memory review process.Experimental results on real datasets show that the proposed model has better recommendation performance than existing recommendation models.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022266/Ebbinghaus forgetting curveattention mechanismreinforcement learningexperience replay |
| spellingShingle | Nan JIN Ruiqin WANG Yuecong LU Ebbinghaus forgetting curve and attention mechanism based recommendation algorithm Dianxin kexue Ebbinghaus forgetting curve attention mechanism reinforcement learning experience replay |
| title | Ebbinghaus forgetting curve and attention mechanism based recommendation algorithm |
| title_full | Ebbinghaus forgetting curve and attention mechanism based recommendation algorithm |
| title_fullStr | Ebbinghaus forgetting curve and attention mechanism based recommendation algorithm |
| title_full_unstemmed | Ebbinghaus forgetting curve and attention mechanism based recommendation algorithm |
| title_short | Ebbinghaus forgetting curve and attention mechanism based recommendation algorithm |
| title_sort | ebbinghaus forgetting curve and attention mechanism based recommendation algorithm |
| topic | Ebbinghaus forgetting curve attention mechanism reinforcement learning experience replay |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022266/ |
| work_keys_str_mv | AT nanjin ebbinghausforgettingcurveandattentionmechanismbasedrecommendationalgorithm AT ruiqinwang ebbinghausforgettingcurveandattentionmechanismbasedrecommendationalgorithm AT yueconglu ebbinghausforgettingcurveandattentionmechanismbasedrecommendationalgorithm |