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Estimating product-choice probabilities from recency and frequency of page views.pdf
Knowledge-Based Systems 99 (2016) 157–167
Contents lists available at ScienceDirect
Knowledge-Based Systems
journal homepage: /locate/knosys
Estimating product-choice probabilities from recency and frequency of page views
Jiro Iwanaga a, Naoki Nishimura b, Noriyoshi Sukegawa c,?, Yuichi Takano d
a Business Intelligence Deployment Center, NTT DATA Mathematical Systems Inc., 1F Shinanomachi Rengakan, 35 Shinanomachi, Shinjyuku-ku, Tokyo 166-0016, Japan b Product Management Unit, Internet Business Development Division, Recruit Lifestyle Co., Ltd., GranTokyo SOUTHTOWER 1-9-2 Marunouchi, Chiyoda-ku, Tokyo 100-6640, Japan c Department of Information and System Engineering, Faculty of Science and Engineering, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan d School of Network and Information, Senshu University, 2-1-1 Higashimita, Tama-ku, Kawasaki-shi, Kanagawa 214-8580, Japan
article info
Article history: Received 11 July 2015 Revised 1 February 2016 Accepted 4 February 2016 Available online 12 February 2016
Keywords: Product choice E-commerce Clickstream data Optimization Recency Frequency Page view
abstract
This paper investigates the relationship between customers’ page views (PVs) and the probabilities of their product choices on e-commerce sites. For this purpose, we create a probability table consisting of product-choice probabilities for all recency and frequency combinations of each customers’ previous PVs. To reduce the estimation error when there are few training samples, we develop optimization models for estimating the product-choice probabilities that satisfy monotonicity, convexity and concavity constraints with respect to recency and frequency. Computational results demonstrate that our method has clear advantages over logistic regression and kernel-based support vector machine.
? 2016 Elsevier B.V. All rights reserved.
1. Introduction
An increasing number of companies are operating e-commerce sites that offer products or services via the
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