  Recommendation system has been widely used in different areas. Collaborative
filtering focuses on rating, ignoring the features of items itself. In order to
effectively evaluate customers preferences on books, taking into consideration
of the characteristics of offline book retail, we use LDA model to calculate
customers preference on book topics and use word2vec to calculate customers
preference on book types. When forecasting rating on books, we take two factors
into consideration: similarity of customers and correlation between customers
and books. The experiment shows that our hybrid recommendation method based on
features performances better than single recommendation method in offline book
retail data.
