PBR: A Personalized Book Resource Recommendation System

Abstract

Recommendation system is widely applied for online resource retrieval, especially in digital publishing industry. A good recommendation system can help the users to efficiently find the desirable reading materials from the massive online resources. However, the conventional recommendation systems are always facing the cold-start problem, and it is difficult to provide the personalized service in an efficient way, since the users’ preference may change sometimes. To address the problems above, this work introduces a personalized book resource recommendation system, which well utilizes the tag information of book resources to interact with the users. The user feedback will deliver their real-time preference, and the system can provide more precise recommendation candidates to improve the service quality. In this demo, we will introduce the overall framework and some important modules of the recommendation system, with relevant technical details. We will show the system functions by providing the visual results of the actual book resource recommendation.

This work is partially supported by Natural Science Foundation of China (Grant No. 61602353), National Social Science Foundation of China (Grant No. 15BGL048), and Natural Science Foundation of Hubei Province (Grant No. 2017CFB505).