Estimating product-choice probabilities from recency and frequency of page views

被引：12

作者：

Iwanaga, Jiro ^{[1
]}

Nishimura, Naoki ^{[2
]}

Sukegawa, Noriyoshi ^{[3
]}

Takano, Yuichi ^{[4
]}

机构：

[1] NTT DATA Math Syst Inc, Business Intelligence Deployment Ctr, Shinjyuku Ku, 1F Shinanomachi Rengakan,35 Shinanomachi, Tokyo 1660016, Japan

[2] Recruit Lifestyle Co Ltd, Prod Management Unit, Internet Business Dev Div, Chiyoda Ku, GranTokyo SOUTHTOWER 1-9-2 Marunouchi, Tokyo 1006640, Japan

[3] Chuo Univ, Dept Informat & Syst Engn, Fac Sci & Engn, Bunkyo Ku, 1-13-27 Kasuga, Tokyo 1128551, Japan

[4] Senshu Univ, Sch Network & Informat, Tama Ku, 2-1-1 Higashimita, Kawasaki, Kanagawa 2148580, Japan

来源：

KNOWLEDGE-BASED SYSTEMS | 2016年 / 99卷

基金：

日本学术振兴会;

关键词：

Product choice; E-commerce; Clickstream data; Optimization; Recency; Frequency; Page view; ISOTONIC REGRESSION; CLICKSTREAM; BEHAVIOR; ALGORITHMS; CUSTOMERS; INTERNET; MODEL; RULE;

D O I：

10.1016/j.knosys.2016.02.006

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：157 / 167

页数：11

共 43 条

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[2]

[Anonymous], 2008, Introduction to information retrieval

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