Deep Learning-Based Text Recommendation Algorithm

In response to the surge in recommendation demand caused by information explosion, this study proposes a rapid text recommendation algorithm based on deep learning. Text features are constructed through word segmentation processing and keyword extraction techniques, combined with word embedding models and TF-IDF weighting analysis to achieve vectorized representation of texts. The LDA topic model is utilized to mine the thematic structure of texts, and the BERT model is employed for sentiment polarity analysis (with an accuracy rate of 90 %). Ultimately, precise recommendations are achieved through cosine similarity matching between user-preferred topics and sentiment orientations. This method provides an innovative solution for intelligent recommendation systems.