数据特征对推荐系统性能的影响

G. Adomavicius, Jingjing Zhang
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引用次数: 130

摘要

本文研究了评级数据特征对几种流行推荐算法性能的影响,包括基于用户和基于项目的协同过滤,以及矩阵分解。我们重点研究了三组数据特征:评级空间、评级频率分布和评级值分布。采用抽样方法获得具有不同特征的不同等级数据子样本;使用推荐算法估计每个样本的预测精度;采用基于线性回归的模型揭示数据特征与推荐准确率之间的关系。在多个评级数据集上的实验结果表明,多个数据特征对推荐准确率的影响一致且显著。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of data characteristics on recommender systems performance
This article investigates the impact of rating data characteristics on the performance of several popular recommendation algorithms, including user-based and item-based collaborative filtering, as well as matrix factorization. We focus on three groups of data characteristics: rating space, rating frequency distribution, and rating value distribution. A sampling procedure was employed to obtain different rating data subsamples with varying characteristics; recommendation algorithms were used to estimate the predictive accuracy for each sample; and linear regression-based models were used to uncover the relationships between data characteristics and recommendation accuracy. Experimental results on multiple rating datasets show the consistent and significant effects of several data characteristics on recommendation accuracy.
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