Review-LLM: A Comprehensive AI Framework for Personalized Review Generation Using Large Language Models and User Historical Data in Recommender Systems
What is the main objective of personalized review generation?
The main objective of personalized review generation is to generate reviews that accurately reflect a user's unique preferences and experiences based on their historical interactions and feedback. This enhances the effectiveness of recommender systems by providing more relevant and personalized reviews.
How do recommender systems benefit from personalized reviews?
Recommender systems benefit from personalized reviews by enhancing their overall effectiveness. Personalized reviews capture users' unique preferences and experiences, which helps in better understanding user satisfaction and dissatisfaction. By incorporating personalized reviews, recommender systems can provide more accurate and relevant recommendations, leading to improved user experience and increased engagement1.
What challenges exist in generating personalized reviews?
Generating personalized reviews poses several challenges, including capturing the subtleties of user satisfaction and dissatisfaction, limited detailed feedback from users, and creating reviews that accurately reflect individual preferences and experiences4. Existing methods often struggle to incorporate user-specific information and sentiment into the review generation process, leading to less personalized and relevant reviews.