Research on Recommendation Model Based on Multi-round Dialogue of Large Language Model
Recently, the recommendation method combined with large language model has shown obvious advantages in improving recommendation accuracy and enhancing user experience. However, these methods do not make full use of user information, and cannot learn the behavioral characteristics of multiple user in...
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| Format: | Article |
| Language: | zho |
| Published: |
Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2025-02-01
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| Series: | Jisuanji kexue yu tansuo |
| Subjects: | |
| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2407087.pdf |
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| Summary: | Recently, the recommendation method combined with large language model has shown obvious advantages in improving recommendation accuracy and enhancing user experience. However, these methods do not make full use of user information, and cannot learn the behavioral characteristics of multiple user interactions by using only a single round of dialogue, and there are huge semantic differences between large language models and recommender systems. In order to solve these problems, this paper proposes a recommendation model based on the multi-round dialogue pattern of large language model. The model uses vector quantization technology to convert user information into user indices, and integrates the language semantics of the large language model with the cooperative semantics of the recommender system through fine-tuning task, which not only learns the user characteristics but also alleviates the problem of semantic differences. The user index and historical interaction data are spliced into prompts, and then recommendations are fine-tuned through multi- round dialogue mechanism to learn the characteristics between user interaction behaviors. Experiments results on Amazon’s three benchmark datasets, such as Instructions, Arts and Games, show that the model is better than the comparison baseline algorithm in the two evaluation indices of hit rate (HR) and normalized discounted cumulative gain (NDCG). Compared with the optimal comparison baseline algorithm on the three datasets, the average increase in HR is 10.53% and the average increase in NDCG is 5.10%, which proves the effectiveness of the model. |
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| ISSN: | 1673-9418 |