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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2022-10-01
|
| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022266/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 1000-0801 |