  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.
