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In order to capture the dynamic interaction between users and items during online shopping and improve the accuracy of recommendation systems(RS),a user rating prediction method combining user preference and item attractiveness is proposed.The reviews are divided into user review texts and product review texts,which are fed into two convolutional neural networks(CNN),and combined with an attention mechanism to dynamically capture semantic and contextual information in the texts,and obtain user and item adaptive representations.Subsequently,using the interactive attention network,the dynamic interaction between the item features and the user features is analyzed to calculate the user preference on specific items and the attractiveness of the items to a specific user.Finally,the prediction module is used to provide accurate predictions about user ratings to unseen items.Results on experimental datasets show that the proposed method achieves optimal performance,with at least 15.1% and 13.6% improvement in MAE and RMSE performance compared to other advanced methods.In addition,the statistical metrics based on Top-K further validate the accuracy of the proposed method for product recommendation.