Two-Tower Structure Recommendation Method Fusing Multi-Source Data

In view of the problem that the recommendation system is not good at integrating multi-source information and user sentiment, this paper proposes a BERT-LSTM Dual-Tower Recommendation Method for Sequential Feature Extraction (BLDRM-SFE). This method uses BERT to extract semantic features from user reviews and item details and obtains vector representations of item IDs and their groups through embedding. The user tower combines user review features with item group features to generate a user vector, while the item tower integrates item detail features with item ID vectors to generate an item vector. The outputs of the two towers are processed by LSTM to handle item ID sequence information, uncover potential sequence relationships, and generate rating predictions, thereby constructing a personalized recommendation list. The experimental results show that this method significantly outperforms four baseline models—BERT4Rec, PRM, BST, and ComiRec—on the Amazon Review Data and Yelp datasets. On the Amazon dataset, BLDRM-SFE improves by 10.39%, 8.08%, 10.78%, 10.59%, and 5.49% across five metrics compared to the baseline models; on the Yelp dataset, the improvements are 10.95%, 10.06%, 13.04%, 12.59%, and 10.8%, respectively. In addition, ablation experiments confirmed the positive impact of item ID sequence information on the method’s performance. The results show that the incorporation of sequence information significantly enhanced the recommendation performance.