Personalized Service System Based on Hybrid Filtering for Digital Library

作     者：高凤荣 邢春晓 杜小勇 王珊 

作者机构：Web and Software Technology R&D Center Research Institute of Information Technology Tsinghua University School of Information Renmin University of China 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2007年第12卷第1期

页      面：1-8页

核心收录：

学科分类：080902[工学-电路与系统] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：the National Natural Science Foundation of China (No. 60473078) 

主　　题：personalized service system content-based filtering collaborative filtering user preferences model category-based collaborative filtering meta-information filtering 

摘      要：Personalized service systems are an effective way to help users obtain recommendations for unseen items, within the enormous volume of information available based on their preferences. The most commonly used personalized service system methods are collaborative filtering, content-based filtering, and hybrid filtering. Unfortunately, each method has its drawbacks. This paper proposes a new method which unified partition-based collaborative filtering and meta-information filtering. In partition-based collaborative filtering the user-item rating matrix can be partitioned into low-dimensional dense matrices using a matrix clustering algorithm. Recommendations are generated based on these low-dimensional matrices. Additionally, the very low ratings problem can be solved using meta-information filtering. The unified method is applied to a digital resource management system. The experimental results show the high efficiency and good performance of the new approach.