A Hybrid Recommendation Method Based on Feature for Offline Book Personalization
This is an incremental improvement for offline book retailers seeking more accurate recommendations by integrating item features.
The authors tackled the problem of personalized book recommendations in offline retail by combining LDA and word2vec to model customer preferences based on book topics and types, resulting in a hybrid method that outperformed single recommendation approaches on their dataset.
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.