A New Adaptive Framework for Collaborative Filtering Prediction.
A New Adaptive Framework for Collaborative Filtering Prediction.
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协作过滤预测的新自适应框架。
DOI：
10.1109/cec.2008.4631164
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Shang,Yi
中科院分区：
文献类型：
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作者：
Almosallam,IbrahimA;Shang,Yi
协同过滤是推荐系统中最成功的技术之一，已被用于亚马逊、TiVo和Netflix等大公司提供的许多商业服务。在本文中，我们专注于基于内存的协同过滤（CF）。现有的CF技术在密集数据上工作得很好，但在稀疏数据上工作得很差。为了解决这一弱点，我们建议使用z分数，而不是明确的评级，并引入一种机制，自适应地结合全局统计数据与基于项目的值的基础上的数据密度水平。我们提出了一个新的自适应框架，封装各种CF算法和它们之间的关系。一个自适应CF预测器的开发，可以自我适应从基于用户的项目为基础的混合方法的基础上的可用评级量。我们的实验结果表明，新的预测器始终获得更准确的预测比现有的CF方法，最显着的改善稀疏数据集。当应用于Netflix Challenge数据集时，我们的方法比现有的CF和奇异值分解（SVD）方法表现得更好，比Netflixpsilas系统提高了4.67%。
Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflixpsilas system.