In the online world, customers can easily navigate to different online stores to make purchases. The products purchased on one site are often associated with product purchases on other sites (e.g., a hotel reservation on one site and a car rental on another site). Whereas market basket analysis is often used to discover associations among products for brick-and-mortar stores, it is rarely applied in the online setting where consumers navigate among different online stores to buy products.
This paper by Assistant Professor Catherine Yang and co-author Chunhui Hao of the Chinese Academy of Sciences addresses a very important question—how to select the right products to promote in order to maximize promotional benefit.
Research Expertise: Information technology, data mining, e-marketing, e-commerce
Discovery of Periodic Patterns in Sequence Data: A Variance-based Approach
INFORMS Journal on Computing, 2011
In this paper, Assistant Professor Catherine Yang and co-authors Balaji Padmanabhan from the University of Southern Florida and Hongyan Liu and Xiaoyu Wang from Tsinghua University address the discovery of periodic patterns in sequence data. Building on prior work in this area, the authors present definitions and new methods for characterizing and identifying four types of periodic patterns.
In this paper, Assistant Professor Catherine Yang and co-author Chunhui Hao from the Chinese Academy of Sciences address a very important question—how to select the right products to promote in order to maximize promotional benefit.
In this paper, Assistant Professor Catherine Yang proposes a simple, yet powerful approach to profile users’ web browsing behavior for the purpose of user identification. The importance of being able to identify users can be significant given a wide variety of applications in electronic commerce, such as product recommendation, personalized advertising, etc.
Toward User Patterns for Online Security: Observation Time and Online User Identification
Decision Support Systems, 2010
Research in biometrics suggests that the time period a specific trait is monitored over (i.e. observing speech or handwriting “long enough”) is useful for identification. Focusing on this aspect, this paper by Assistant Professor Catherine Yang and co-author Balaji Padmanabhan from the University of South Florida presents a data mining analysis of the effect of observation time period on user identification based on online user behavior.
In her recently published book, The Online Customer: New Data Mining and Marketing Approaches, Assistant Professor Catherine Yang details how data mining and marketing approaches can be used to study and solve Web marketing problems. The book uses a vast dataset of Web transactions from the largest online retailers, including Amazon.com. In particular, Yang shows how to integrate and compare statistical methods from marketing and data mining research.