Paper Number
1704
Paper Type
Complete
Description
The New Community Cold-Start problem refers to the serious challenge of building recommender system faced by a newly established community, where there is a lack of users and user engagement. To tackle this problem, we propose a novel recommendation method, leveraging the inference capability of LLM. We choose In-Context-Learning as our prompting strategy and design a coarse-to-fine framework to choose demonstration examples to create effective ICL prompts efficiently. We leverage the bias exhibited by LLM in perceiving prompts in the coarse stage and a reinforcement learning model to learn an example selection policy in the fine stage. Empirical studies demonstrate our method can greatly improve the performance of recommendation for the NCCS problem.
Recommended Citation
Che, Shangkun; Mao, Minjia; and Liu, Hongyan, "New Community Cold-Start Recommendation: A Novel Large Language Model-based Method" (2024). ICIS 2024 Proceedings. 6.
https://aisel.aisnet.org/icis2024/data_soc/data_soc/6
New Community Cold-Start Recommendation: A Novel Large Language Model-based Method
The New Community Cold-Start problem refers to the serious challenge of building recommender system faced by a newly established community, where there is a lack of users and user engagement. To tackle this problem, we propose a novel recommendation method, leveraging the inference capability of LLM. We choose In-Context-Learning as our prompting strategy and design a coarse-to-fine framework to choose demonstration examples to create effective ICL prompts efficiently. We leverage the bias exhibited by LLM in perceiving prompts in the coarse stage and a reinforcement learning model to learn an example selection policy in the fine stage. Empirical studies demonstrate our method can greatly improve the performance of recommendation for the NCCS problem.
Comments
13-DataAnalytics