Authors:
Dominic Stange
1
;
Michael Kotzyba
2
;
Stefan Langer
2
and
Andreas Nürnberger
2
Affiliations:
1
Volkswagen AG, Germany
;
2
University of Magdeburg, Germany
Keyword(s):
Web Usage Mining, Interaction Log Analysis, Exploratory Search, Recommender Systems, Interactive Information Retrieval, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Collaborative and Social Interaction
;
Collaborative Computing
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
HCI on Enterprise Information Systems
;
Human-Computer Interaction
;
Industrial Applications of Artificial Intelligence
;
Intelligent Agents
;
Internet Agents
;
Internet Technology
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Software Agents and Internet Computing
;
Symbolic Systems
;
User Profiling and Recommender Systems
;
Web Information Systems and Technologies
Abstract:
In this paper we introduce a novel approach for modeling and interpreting search behavior for exploratory
search by using a so called exploration graph. We use an existing methodology of logging and analyzing user
interactions with a web browser and add an additional interpretation step that can be used, e. g. to integrate
sensemaking or browsing patterns into the log data. We conducted a user study and are able to show that:
(a) interaction logs can be interpreted semantically, (b) semantic interpretations lead to a more connected
exploration graph, and (c) multiple (even contradicting) interpretations of the same search behavior may exist
at the same time. We also show how our theoretical model can be applied in the area of professional search by
incorporating insights gained from the model into novel recommendation and machine learning approaches.