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A framework for representing navigational patterns as full temporal objects

A framework for representing navigational patterns as full temporal objects Navigational patterns have applications in several areas including: web personalization, recommendation, user-profiling and clustering, etc. Most existing works on navigational pattern-discovery give little consideration to the effects of time (or temporal trends) on navigational patterns. Some recent works have proposed frameworks for partial temporal representation of navigational patterns. This paper proposes a framework that models navigational patterns as full temporal objects that may be represented as time series. Such a representation allows a rich array of analysis techniques to be applied to the data. The proposed framework also enhances the understanding and interpretation of discovered patterns, and provides a rich environment for integrating the analysis of navigational patterns with data from the underlying organizational environments and other external factors. Such integrated analysis is very helpful in understanding navigational patterns (e.g., E-commerce sites may integrate the trend analysis of navigational patterns with other market data and economic indicators). To achieve full temporal representation, this paper proposes a navigational pattern-discovery technique that is not based on pre-defined thresholds. This is a shift from existing techniques that are driven by pre-defined thresholds that can only support partial temporal representation of navigational patterns. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGecom Exchanges Association for Computing Machinery

A framework for representing navigational patterns as full temporal objects

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2004 by ACM Inc.
ISSN
1551-9031
DOI
10.1145/1120687.1120691
Publisher site
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Abstract

Navigational patterns have applications in several areas including: web personalization, recommendation, user-profiling and clustering, etc. Most existing works on navigational pattern-discovery give little consideration to the effects of time (or temporal trends) on navigational patterns. Some recent works have proposed frameworks for partial temporal representation of navigational patterns. This paper proposes a framework that models navigational patterns as full temporal objects that may be represented as time series. Such a representation allows a rich array of analysis techniques to be applied to the data. The proposed framework also enhances the understanding and interpretation of discovered patterns, and provides a rich environment for integrating the analysis of navigational patterns with data from the underlying organizational environments and other external factors. Such integrated analysis is very helpful in understanding navigational patterns (e.g., E-commerce sites may integrate the trend analysis of navigational patterns with other market data and economic indicators). To achieve full temporal representation, this paper proposes a navigational pattern-discovery technique that is not based on pre-defined thresholds. This is a shift from existing techniques that are driven by pre-defined thresholds that can only support partial temporal representation of navigational patterns.

Journal

ACM SIGecom Exchanges – Association for Computing Machinery

Published: Nov 1, 2004

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