Personalized Book Recommender System Based on Chinese Library Classification

Abstract

with the continuous construction and development of university library, how to find interesting books from the massive books is becoming a concerned problem. In this paper, we develop a personalized book recommender system based on Chinese Library Classification Method named CLCM. CLCM uses Upper and Lower Level Relations Model (ULLRM) to describe the characteristic words and fuses the Dominant and Recessive Feedback Model (DRFM) to update the users' preferences. And visualization of book inquiry improves the efficiency of inquiring. The experimental results show that CLCM performs much better than the state-of-the art approaches in the university library.