推荐系统中的冷启动问题研究综述  

Survey of Cold-start Problem in Collaborative Filtering Recommender System

作　　者：孙冬婷[1] 何涛[2] 张福海[3] 

机构地区：[1]国防科学技术大学计算机学院 [2]中国人民解放军69031部队 [3]解放军南京政治学院理论系

出　　处：《计算机与现代化》2012年第5期59-63,共5页Computer and Modernization

摘　　要：推荐系统能够快捷、准确地定位用户真正需要的信息,解决网络信息过载问题。其中协同过滤推荐技术是推荐系统应用最广泛和成功的技术,但该技术面临冷启动问题的挑战。本文分析冷启动问题的产生原因,阐述研究冷启动问题的意义,重点总结解决冷启动问题的算法现状,分析比较它们的性能差异和各自存在的优缺点,从而便于使用者在解决冷启动问题时对算法的选择和使用。Recommender systems have been widely used as an important response to information overload problem which leads consumers to locate the right information at the right time accurately and rapidly.The most successful and popular technique of such systems is collaborative filtering.However,collaborative filtering faces a key challenge of the cold-start problem.This paper first illustrates the cause of the cold-start problem and then tables the significance of cold-start problem research.The core of this paper is to summarize the existing algorithms which are used for addressing the cold-start problem.In order to facilitate users to choose the right algorithm to tackle the cold-start problem in collaborative filtering,it compares the performance and respective advantage and disadvantage of different algorithms.

关 键 词：推荐系统 协同过滤 冷启动 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]