  Recommendation systems help users find matched items based on their previous
behaviors. Personalized recommendation becomes challenging in the absence of
historical user-item interactions, a practical problem for startups known as
the system cold-start recommendation. While existing research addresses
cold-start issues for either users or items, we still lack solutions for system
cold-start scenarios. To tackle the problem, we propose PromptRec, a simple but
effective approach based on in-context learning of language models, where we
transform the recommendation task into the sentiment analysis task on natural
language containing user and item profiles. However, this naive approach
heavily relies on the strong in-context learning ability emerged from large
language models, which could suffer from significant latency for online
recommendations. To solve the challenge, we propose to enhance small language
models for recommender systems with a data-centric pipeline, which consists of:
(1) constructing a refined corpus for model pre-training; (2) constructing a
decomposed prompt template via prompt pre-training. They correspond to the
development of training data and inference data, respectively. The pipeline is
supported by a theoretical framework that formalizes the connection between
in-context recommendation and language modeling. To evaluate our approach, we
introduce a cold-start recommendation benchmark, and the results demonstrate
that the enhanced small language models can achieve comparable cold-start
recommendation performance to that of large models with only $17\%$ of the
inference time. To the best of our knowledge, this is the first study to tackle
the system cold-start recommendation problem. We believe our findings will
provide valuable insights for future works. The benchmark and implementations
are available at https://github.com/JacksonWuxs/PromptRec.
