Recommendation Algorithms Combining Comment Text Semantics and Occurrence of Commodity

The recommendation method of integrating com-ment text attempts to use user comments as auxiliary data sources to recommend to users. This article proposes a deep learning model Context2Rec to improve the interactivity between products and meet the needs of user multi feature learning. This model utilizes comment text to discover important word order and contextual information of words in the text, and improves semantic representation through pre-training the model. It inno-vatively combines comment text features and sequence features to enhance the performance of recommendation systems. The model effectively captures dynamic changes in user preferences by using BERT for semantic understanding and combining item2vec for association feature learning. Tested on different datasets, the experimental results show that compared with other algorithms, this model effectively improves the recommendation performance, providing a novel method for personalized recommendation services.