NDLI: Personalization from incomplete data: what you don't know can hurt

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Personalization from incomplete data: what you don't know can hurt

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Personalization from incomplete data: what you don't know can hurt

Content Provider	ACM Digital Library
Author	Kimbrough, Steven O. Zheng, Zhiqiang Padmanabhan, Balaji
Abstract	Clickstream data collected at any web site (site-centric data) is inherently incomplete, since it does not capture users' browsing behavior across sites (user-centric data). Hence, models learned from such data may be subject to limitations, the nature of which has not been well studied. Understanding the limitations is particularly important since most current personalization techniques are based on site-centric data only. In this paper, we empirically examine the implications of learning from incomplete data in the context of two specific problems: (a) predicting if the remainder of any given session will result in a purchase and (b) predicting if a given user will make a purchase at any future session. For each of these problems we present new algorithms for fast and accurate data preprocessing of clickstream data. Based on a comprehensive experiment on user-level clickstream data gathered from 20,000 users' browsing behavior, we demonstrate that models built on user-centric data outperform models built on site-centric data for both prediction tasks.
Starting Page	154
Ending Page	163
Page Count	10
File Format	PDF
ISBN	158113391X
DOI	10.1145/502512.502535
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2001-08-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Learning Clickstream data Personalization Probabilistic clipping Data preprocessing Incomplete data
Content Type	Text
Resource Type	Article

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