Research on Full-text Recommendation System Based on Language Technology Applied In Library

In recent years, recommendation systems have been widely used in the commercial field, and many e-commerce platforms and merchants have adopted the model of recommendation systems as a way to sell goods. However, when the product's features are insufficient, or the user's evaluation of the product is not much, it is difficult for the recommender system to recommend the product that meets the customer's expectations. For example, when recommending books in the library, you will find out that the books cannot be like clothes and can use rich and diverse ontology features such as brand, material, color, and length as the basis for the recommendation system. In addition, we found that only a few users will Evaluate or comment on books during the user's borrowing process. To sum up, this study will explore how to recommend books through natural language processing in the case of insufficient book features and insufficient user ratings and reviews.