Text Recommendation Algorithm Fused with BERT Semantic Information

Abstract

Faced with the problem of text recommendation with massive data on the Internet, the use of a recommendation method based on deep learning combined with semantic information will improve the accuracy of the recommendation results. Therefore, we propose a HyReB (Hybrid Recommendation algorithm combining BERT and CNN network). The algorithm HyReB uses the BERT word vector as the input of the CNN network and incorporates external semantic information in features extraction and topic classification. Then we combine BERT and TextRank algorithms to extract text keywords and calculate the BERT word vector similarity of topic word. Finally, we do the weighted calculation of the label proportion of the recommended text and the similarity of the topic word vector to make the text top-N recommendation. The HyReB algorithm makes user interest extraction more refined and incorporates BERT semantic information into the text recommendation. Experiments show that the feature extraction of HyReB is more accurate and has a better recommendation effect when performing small-scale accurate text recommendation.