  Product review generation is an important task in recommender systems, which
could provide explanation and persuasiveness for the recommendation. Recently,
Large Language Models (LLMs, e.g., ChatGPT) have shown superior text modeling
and generating ability, which could be applied in review generation. However,
directly applying the LLMs for generating reviews might be troubled by the
``polite'' phenomenon of the LLMs and could not generate personalized reviews
(e.g., negative reviews). In this paper, we propose Review-LLM that customizes
LLMs for personalized review generation. Firstly, we construct the prompt input
by aggregating user historical behaviors, which include corresponding item
titles and reviews. This enables the LLMs to capture user interest features and
review writing style. Secondly, we incorporate ratings as indicators of
satisfaction into the prompt, which could further improve the model's
understanding of user preferences and the sentiment tendency control of
generated reviews. Finally, we feed the prompt text into LLMs, and use
Supervised Fine-Tuning (SFT) to make the model generate personalized reviews
for the given user and target item. Experimental results on the real-world
dataset show that our fine-tuned model could achieve better review generation
performance than existing close-source LLMs.
