基于用户相似性传递的跨平台交叉推荐算法

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中文信息学报 ›› 2016, Vol. 30 ›› Issue (2) : 90-98.

情感分析与社会计算

基于用户相似性传递的跨平台交叉推荐算法

李超^1,2, 周涛^1,2, 黄俊铭³,程学旗³,沈华伟³

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Transfer with Shared Users: A Cross-platform

LI Chao^1,2, ZHOU Tao¹, HUANG Junming³, CHENG Xueqi³, SHEN Huawei³

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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。

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.

导出引用

李超,周涛, 黄俊铭,程学旗,沈华伟. 基于用户相似性传递的跨平台交叉推荐算法. 中文信息学报. 2016, 30(2): 90-98

LI Chao, ZHOU Tao, HUANG Junming, CHENG Xueqi, SHEN Huawei. Transfer with Shared Users: A Cross-platform. Journal of Chinese Information Processing. 2016, 30(2): 90-98

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基金

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

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2013-09-15	2016-04-20
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