SpringerLink - Journal Article

Understanding collaborative filtering parameters for personalized recommendations in e-commerce

Hong Joo Lee¹, Jong Woo Kim² and Sung Joo Park³

(1)	School of Business Administration, Catholic University of Korea, Seoul, South Korea

(2)	School of Business, Hanyang University, Seoul, South Korea

(3)	Graduate School of Management, Korea Advanced Institute of Science and Technology, Seoul, South Korea

Published online: 24 October 2007

Abstract 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.

Keywords Collaborative filtering - e-Commerce - Parameter selection - Personalization

Hong Joo Lee
Email: hongjoo@catholic.ac.kr

Jong Woo Kim (Corresponding author)
Email: kjw@hanyang.ac.kr

Sung Joo Park
Email: sjpark@kgsm.kaist.ac.kr

Hong Joo Lee Full-time Lecturer, School of Business Administration, Catholic University of Korea, Seoul, Korea.

Hong Joo Lee is a full-time lecturer at the School of Business Administration, Catholic University of Korea. He received his M.S. and Ph.D. degrees in 1999 and 2006, respectively, from the Graduate School of Management at Korea Advanced Institute of Science and Technology (KAIST). He was a post doctoral fellow at the Center for Collective Intelligence, MIT Sloan School of Management. His research areas are personalization, intelligent information systems, and virtual collaboration.

Jong Woo Kim Associate Professor, School of Business, Hanyang University, Seoul, Korea.

Jong Woo Kim is an associate professor of information systems at the School of Business, Hanyang University, Seoul, Korea. He received his M.S. and Ph.D. degrees in 1991 and 1995, respectively, from the Department of Management Science, the Department of Industrial Management at Korea Institute of Science and Technology (KAIST), Korea. His current research interests include intelligent information systems, e-commerce recommendation systems, data mining applications, and business process modeling and integration.

Sung Joo Park Professor, Graduate School of Management, KAIST (Korea Advanced Institute of Science and Technology), Seoul, Korea.

Sung Joo Park is a Professor of Information Systems at the Graduate School of Management, KAIST in Seoul, Korea. He holds a B.S. degree in Industrial Engineering from the Seoul National University, an M.S. in Industrial Engineering from the Korea Advanced Institute of Science, and a Ph.D. in Systems Science from the Michigan State University. He has been a senior researcher at the Software Development Center, KIST, and a professor at the KAIST since 1990. His areas of research interests include intelligent information systems and the application of agent technology to management decision-making.

Understanding collaborative filtering parameters for personalized recommendations in e-commerce

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