Detecting change in categorical data: mining contrast sets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding related pages in the World Wide Web
WWW '99 Proceedings of the eighth international conference on World Wide Web
Personalization from incomplete data: what you don't know can hurt
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Graphic and numerical methods to access navigation in hypertext
International Journal of Human-Computer Studies
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Effects of scent and breadth on use of site-specific search on e-commerce Web sites
ACM Transactions on Computer-Human Interaction (TOCHI)
The Journal of Machine Learning Research
Segmenting Customer Transactions Using a Pattern-Based Clustering Approach
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Modeling the Clickstream: Implications for Web-Based Advertising Efforts
Marketing Science
Building Association-Rule Based Sequential Classifiers for Web-Document Prediction
Data Mining and Knowledge Discovery
On the Depth and Dynamics of Online Search Behavior
Management Science
Dynamic Conversion Behavior at E-Commerce Sites
Management Science
Modeling Browsing Behavior at Multiple Websites
Marketing Science
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
When the Wait Isnt So Bad: The Interacting Effects of Website Delay, Familiarity, and Breadth
Information Systems Research
Modeling Online Browsing and Path Analysis Using Clickstream Data
Marketing Science
Online Consumer Search Depth: Theories and New Findings
Journal of Management Information Systems
Predicting On-Line Task Completion with Clickstream Complexity Measures: A Graph-Based Approach
International Journal of Electronic Commerce
Contrasting the Contrast Sets: An Alternative Approach
IDEAS '07 Proceedings of the 11th International Database Engineering and Applications Symposium
Scents in Programs: Does Information Foraging Theory Apply to Program Maintenance?
VLHCC '07 Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing
Scented Widgets: Improving Navigation Cues with Embedded Visualizations
IEEE Transactions on Visualization and Computer Graphics
Information Foraging Theory: Adaptive Interaction with Information
Information Foraging Theory: Adaptive Interaction with Information
Allocating time across multiple texts: sampling and satisficing
Human-Computer Interaction
Information Systems Research
Predicting consumer sentiments from online text
Decision Support Systems
Mining comparative opinions from customer reviews for Competitive Intelligence
Decision Support Systems
Communications of the ACM
Browsing large pictures under limited display sizes
IEEE Transactions on Multimedia
Context-aware music recommendation based on latenttopic sequential patterns
Proceedings of the sixth ACM conference on Recommender systems
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The long tail has attracted substantial theoretical as well as practical interest, yet there have been few empirical studies that have explicitly examined the factors that drive online conversions at these sites. This research tests several hypotheses derived from Information Foraging Theory (IFT) that pertain to goal achievement on long tail Web sites. IFT introduced concepts of information patches and information scent to model information seeking behavior of individuals, but has mostly been tested in production rule environments where the theory is used to simulate user behavior. Testing IFT-driven hypotheses on real data required learning information patches and scents using an inductive approach and in this paper we adapt existing algorithms for these discovery tasks. Our results based on clickstream data from forty-seven small business Web sites show both the existence of valuable information patches and information scent trails as well as their importance in explaining conversion on these sites. The majority of the hypotheses were supported and we discuss the implications of this for researchers and practitioners.