Statistical Analysis for Engineers and Scientists: A Computer-Based Approach (IBM)
Statistical Analysis for Engineers and Scientists: A Computer-Based Approach (IBM)
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
A Survey of Context-Aware Mobile Computing Research
A Survey of Context-Aware Mobile Computing Research
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Segmenting Customers from Population to Individuals: Does 1-to-1 Keep Your Customers Forever?
IEEE Transactions on Knowledge and Data Engineering
What can context do for web services?
Communications of the ACM - Software product line
Personalization in Context: Does Context Matter When Building Personalized Customer Models?
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Human-Computer Interaction
How to use enriched browsing context to personalize web site access
CONTEXT'03 Proceedings of the 4th international and interdisciplinary conference on Modeling and using context
Understanding context before using it
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
Unsupervised clustering of context data and learning user requirements for a mobile device
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
Identification of textual contexts
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
Exploiting rich context: an incremental approach to context-based web search
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
Granularity as a parameter of context
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
Modeling context as statistical dependence
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
Exploiting contextual information in recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
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In e-commerce applications, no systematic research has been provided to evaluate if the use of a detailed and rich contextual representation improves the user modeling predictive performances. An underestimated issue is also evaluating if context could be inferred by existing customer data off-line, in spite of getting the customer involved on-line in the gathering process. In this paper, we address those problems, defining context as "the intent of" a customer purchase. To this aim, we collected data containing rich contextual information, hierarchically structured, by developing a special-purpose browser. The experimental results show that the finer the granularity of contextual information the better is the modeling of customers' behavior. Representing the context in a hierarchical structure is a necessary condition, for inferring the context off-line, but it's not a sufficient one.