SEMANTIC-ENHANCED PERSONALIZED RECOMMENDER SYSTEM

摘要：

Personalized recommender systems have emerged as a powerful method for improving both the content of customers and the profit of providers in e-business environment.Nowadays, many kinds of recom mender methods have been proposed to provide personalized services.However, all these techniques have not made full use of the semantic information of objects, which leading them to an unsatisfying performance.Collaborative filter (CF) system, as the most popular personalized recommender systems, has such well-known limitations as sparsity, scalability and cold-start problem.A semantic-enhanced collaborative recommender system is proposed in this paper.The semantic information of objects is extracted to support the recommendation process.This study compares the performance of the proposed technique with the traditional CF approaches.Experimental results demonstrate the effectiveness of the proposed method.

关键词： Personalized recommendation Semantic information Collaborative filter Clustering Ontology

作者: RUI-QIN WANG FAN-SHENG KONG

作者单位: Artificial Intelligence Institute, Zhejiang University, Hangzhou 310027, China;School of Computer Sc Artificial Intelligence Institute, Zhejiang University, Hangzhou 310027, China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 4069-4074

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）