Experiences in Using CC/PP in Context-Aware Systems
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
ISWC '97 Proceedings of the 1st IEEE International Symposium on Wearable Computers
A data-oriented survey of context models
ACM SIGMOD Record
Contextual advertising by combining relevance with click feedback
Proceedings of the 17th international conference on World Wide Web
Using Context to Improve Predictive Modeling of Customers in Personalization Applications
IEEE Transactions on Knowledge and Data Engineering
Human-Computer Interaction
Matchbox: large scale online bayesian recommendations
Proceedings of the 18th international conference on World wide web
Context-aware query classification
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Context-aware ranking in web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Fast context-aware recommendations with factorization machines
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Identifying hidden contexts in classification
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?
Expert Systems with Applications: An International Journal
Web-scale user modeling for targeting
Proceedings of the 21st international conference companion on World Wide Web
Discovering temporal hidden contexts in web sessions for user trail prediction
Proceedings of the 22nd international conference on World Wide Web companion
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Predictive Web Analytics is aimed at understanding behavioural patterns of users of various web-based applications: e-commerce, ubiquitous and mobile computing, and computational advertising. Within these applications business decisions often rely on two types of predictions: an overall or particular user segment demand predictions and individualised recommendations for visitors. Visitor behaviour is inherently sensitive to the context, which can be defined as a collection of external factors. Context-awareness allows integrating external explanatory information into the learning process and adapting user behaviour accordingly. The importance of context-awareness has been recognised by researchers and practitioners in many disciplines, including recommendation systems, information retrieval, personalisation, data mining, and marketing. We focus on studying ways of context discovery and its integration into predictive analytics.