基于用户相似性传递的跨平台交叉推荐算法

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摘要个性化推荐系统在电子商务领域中的广泛应用带来了巨大的经济效益和良好的用户体验。由于用户数据往往分布在多个不同的网站,单个网站的推荐系统受制于数据稀疏性的限制,难以获得准确的推荐效果。该文提出了一种基于传递相似性的交叉推荐系统算法,可以利用多个网站平台数据计算不同网站中的用户的相似度,从而很大程度上克服了推荐系统中的数据稀疏性以及冷启动问题。结果显示,该交叉推荐算法与传统的针对单个数据集的推荐算法相比,推荐的精确性有一至两倍的提高。

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	李超
	周涛
	黄俊铭
	程学旗
	沈华伟

关键词 ：个性化推荐系统, 协同过滤, 多源数据, 稀疏性, 冷启动

Abstract：The widely use of personalized recommender systems on online shopping websites results in great profits and enhanced user experiences. However, since a users behaviors usually scatter cross multiple different websites, it becomes difficult to provide accurate recommendations when a recommender system sees a section of his behaviors on a single website. We propose a new recommendation algorithm that transfers behaviors across different websites to calculate similarities between users on different websites. Our algorithm overcomes the sparsity and cold-start problem in recommender systems with a significant accuracy improvment, outperforming traditional algorithms that applied on a single website only.

Key words： personalization recommender systems collaborative filtering multiple source datasets sparsity cold-start problem

收稿日期: 2013-09-15

中图分类号:

TP391

基金资助:国家基础研究发展计划(973)(2012CB316303,2013CB329602);国家自然科学基金(61232010,61202215)

作者简介: 李超(1988—),硕士,主要研究领域为社交媒体分析、数据挖掘、机器学习。E-mail: xunhuan.lc@gmail.com
周涛(1982—),博士,教授,主要研究领域为统计物理与复杂性科学。E-mail: zhutou@ustc.edu黄俊铭(1984—),博士,主要研究领域为信息传播、社交网络分析。E-mail: mail@junminghuang.com

链接本文:

http://jcip.cipsc.org.cn/CN/Y2016/V30/I2/90

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