Deep Learning Recommendation Algorithm Based on Reviews and Item Descriptions

Reviews contain rich user and item information, which helps to alleviate the problem of data sparsity.However, the existing recommendation model based on reviews is not sufficient and effective enough to mine the review texts, and most of them ignore the migration of user interest over time and the item description documents containing the item attribute, which makes the recommendation result not accurate enough.In this paper, a deep semantic mining based recommendation model（DSMR） is proposed.By mining the semantic information of review texts and item description documents in depth, user characteristics and item attributes can be extracted more accurately, so as to realize more accurate recommendation.Firstly, the BERT pre-training model is used to process the comment text and item description document, and the user characteristics and item attributes are excavated deeply, which effectively alleviated the problems of data sparse and item cold start.Then, the forward LSTM is used to pay attention to the chang