Recommending cold items is a long-standing challenge for collaborative filtering models because these cold items lack historical user interactions to model their collaborative features. The gap between the content of cold items and their behavior patterns makes it difficult to generate accurate behavioral embeddings for cold items. Existing cold-start models use mapping functions to generate fake behavioral embeddings based on the content feature of cold items. However, these generated embeddings have significant differences from the real behavioral embeddings, leading to a negative impact on cold recommendation performance. To address this challenge, we propose an LLM Interaction Simulator (LLM-InS) to model users' behavior patterns based on the content aspect. This simulator allows recommender systems to simulate vivid interactions for each cold item and transform them from cold to warm items directly. Specifically, we outline the designing and training process of a tailored LLM-simulator that can simulate the behavioral patterns of users and items. Additionally, we introduce an efficient "filtering-and-refining" approach to take full advantage of the simulation power of the LLMs. Finally, we propose an updating method to update the embeddings of the items. we unified trains for both cold and warm items within a recommender model based on the simulated and real interactions. Extensive experiments using real behavioral embeddings demonstrate that our proposed model, LLM-InS, outperforms nine state-of-the-art cold-start methods and three LLM models in cold-start item recommendations.

翻译：冷物品推荐是协同过滤模型长期面临的挑战，因为这类物品缺乏历史用户交互来建模其协同特征。冷物品内容与其行为模式之间的差距，使得为冷物品生成准确的行为嵌入变得困难。现有冷启动模型使用映射函数，基于冷物品的内容特征生成虚假行为嵌入，但这些生成的嵌入与真实行为嵌入存在显著差异，对冷推荐性能产生负面影响。为解决这一挑战，我们提出了一种大语言模型交互模拟器（LLM-InS），从内容层面建模用户行为模式。该模拟器允许推荐系统为每个冷物品模拟生动的交互，并直接将其从冷物品转化为热物品。具体而言，我们概述了定制化大语言模型模拟器的设计与训练过程，该模拟器能够模拟用户和物品的行为模式。此外，我们引入了一种高效的“过滤-精炼”方法，以充分利用大语言模型的模拟能力。最后，我们提出了一种更新方法用于更新物品嵌入，并基于模拟交互与真实交互，在推荐模型中对冷热物品进行统一训练。使用真实行为嵌入的大量实验表明，我们提出的模型LLM-InS在冷启动物品推荐中优于九种最先进的冷启动方法和三种大语言模型。