Application of Dimensionality Reduction in Recommender System - A Case Study
Application of Dimensionality Reduction in Recommender System - A Case Study
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DOI：
10.21236/ada439541
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发表时间：
2000-07
影响因子：
7.7
通讯作者：
B. Sarwar;G. Karypis;J. Konstan;J. Riedl
中科院分区：
文献类型：
--
作者：
B. Sarwar;G. Karypis;J. Konstan;J. Riedl
摘要：我们调查了减少维数的使用来提高名为“推荐系统”推荐系统的新类别数据分析软件的性能，将知识发现技术应用于实时客户互动期间提出产品建议的问题。如今，这些系统在电子商务中取得了广泛的成功，尤其是随着互联网的出现。客户和产品的巨大增长对电子商务领域的推荐系统提出了三个关键挑战。这些是：提出高质量的建议，每秒为数百万的客户和产品执行许多建议，并在数据稀疏时获得高覆盖范围。一个成功的推荐系统技术是协作过滤，该技术通过将客户的喜好与其他客户匹配，以提出建议。已显示协作过滤可以产生高质量的建议，但性能随客户和产品数量而降低。需要新的推荐系统技术，即使对于非常大的问题，也可以快速产生高质量的建议。本文介绍了两个不同的实验，我们探索了一种称为单数值分解（SVD）的技术，以降低推荐系统数据库的维度。每个实验都使用SVD与推荐系统的质量与使用协作过滤的推荐系统的质量进行比较。第一个实验比较了两个推荐系统在基于产品显式评级数据库预测消费者偏好方面的有效性。第二个实验比较了两个推荐系统在基于电子商务网站的现实客户购买数据库中生成顶级N列表中的有效性。我们的经验表明，在某些条件下，SVD有可能应对推荐系统的许多挑战。
Abstract : We investigate the use of dimensionality reduction to improve performance for a new class of data analysis software called "recommender systems" Recommender systems apply knowledge discovery techniques to the problem of making product recommendations during a live customer interaction. These systems are achieving widespread success in E-commerce nowadays, especially with the advent of the Internet. The tremendous growth of customers and products poses three key challenges for recommender systems in the E-commerce domain. These are: producing high quality recommendations, performing many recommendations per second for millions of customers and products, and achieving high coverage in the face of data sparsity. One successful recommender system technology is collaborative filtering, which works by matching customer preferences to other customers in making recommendations. Collaborative filtering has been shown to produce high quality recommendations, but the performance degrades with the number of customers and products. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very largescale problems. This paper presents two different experiments where we have explored one technology called Singular Value Decomposition (SVD) to reduce the dimensionality of recommender system databases. Each experiment compares the quality of a recommender system using SVD with the quality of a recommender system using collaborative filtering. The first experiment compares the effectiveness of the two recommender systems at predicting consumer preferences based on a database of explicit ratings of products. The second experiment compares the effectiveness of the two recommender systems at producing Top-N lists based on a real-life customer purchase database from an E-Commerce site. Our experience suggests that SVD has the potential to meet many of the challenges of recommender systems, under certain conditions.