  This paper addresses the gap between general-purpose text embeddings and the
specific demands of item retrieval tasks. We demonstrate the shortcomings of
existing models in capturing the nuances necessary for zero-shot performance on
item retrieval tasks. To overcome these limitations, we propose generate
in-domain dataset from ten tasks tailored to unlocking models' representation
ability for item retrieval. Our empirical studies demonstrate that fine-tuning
embedding models on the dataset leads to remarkable improvements in a variety
of retrieval tasks. We also illustrate the practical application of our refined
model in a conversational setting, where it enhances the capabilities of
LLM-based Recommender Agents like Chat-Rec. Our code is available at
https://github.com/microsoft/RecAI.
