User Rating Prediction Method Based on Fine-tuning of Large Language Models

Online reviews in social networks reflect users' preferences for specific attributes of products. Accurate predictions of user ratings based on these reviews can help businesses better understand genuine user feedback. The purpose of this study is to fine-tune large language models using online reviews and corresponding user rating data, generating a large model for predicting user ratings based on reviews. An attention mechanism is introduced to calculate attention weights for fine-grained review texts, reflecting the contribution of different text features to user rating prediction. By visualizing these weights, the process of calculating the predicted rating values can be explained. Experimental results show that the proposed interpretable user rating prediction method can effectively visualize the attention weights of important text features in the decision-making process of the large rating prediction model. Compared to the baseline model, the mean absolute error is reduced by 1.96, and the root mean square error is reduced by 1.73.