Understanding collaborative filtering parameters for personalized recommendations in e-commerce

被引：9

作者：

Lee H.J. ^{[1
]}

Kim J.W. ^{[2
]}

Park S.J. ^{[3
]}

机构：

[1] School of Business Administration, Catholic University of Korea, Seoul

[2] School of Business, Hanyang University, Seoul

[3] Graduate School of Management, Korea Advanced Institute of Science and Technology, Seoul

来源：

Electronic Commerce Research | 2007年 / 7卷 / 3-4期

关键词：

Collaborative filtering; E-Commerce; Parameter selection; Personalization;

D O I：

10.1007/s10660-007-9004-7

中图分类号：

学科分类号：

摘要：

Collaborative Filtering (CF) is a popular method for personalizing product recommendations for e-Commerce and customer relationship management (CRM). CF utilizes the explicit or implicit product evaluation ratings of customers to develop personalized recommendations. However, there has been no in-depth investigation of the parameters of CF in relation to the number of ratings on the part of an individual customer and the total number of ratings for an item. We empirically investigated the relationships between these two parameters and CF performance, using two publicly available data sets, EachMovie and MovieLens. We conducted three experiments. The first two investigated the relationship between a particular customer's number of ratings and CF recommendation performance. The third experiment evaluated the relationship between the total number of ratings for a particular item and CF recommendation performance. We found that there are ratings thresholds below which recommendation performance increases monotonically, i.e., when the numbers of customer and item ratings are below threshold levels, CF recommendation performance is affected. In addition, once rating numbers surpass threshold levels, the value of each rating decreases. These results may facilitate operational decisions when applying CF in practice. © Springer Science+Business Media, LLC 2007.

引用

页码：293 / 314

页数：21

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