Julia Kiseleva
ABSTRACT
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.
- G. D. Abowd, A. K. Dey, P. J. Brown, N. Davies, M. Smith, and P. Steggles. Towards a better understanding of context and context-awareness. 1999.Google Scholar
Cross Ref
- G. Adomavicius and A. Tuzhilin. Context-aware recommender systems. CARS, 2010.Google Scholar
- M. Aly, A. Hatch, V. Josifovski, and V. K. Narayanan. Web-scale user modeling for targeting. In WWW, 2012. Google ScholarDigital Library
- C. Bolchini, C. A. Curino, E. Quintarelli, F. A. Schreiber, and L. Tanca. A data-oriented survey of context models. In SIGMOD, 2007. Google ScholarDigital Library
- P. Brown, J. Bovey, and X. Chen. Context-aware applications: From the laboratory to the marketplace. IEEE Personal Comm, 4:58--64, 1997.Google ScholarCross Ref
- H. Cao, D. H. Hu, D. Shen, D. Jiang, J.-T. Sun, E. Chen, and Q. Yang. Context-aware query classification. In SIGIR, 2009. Google ScholarDigital Library
- D. Chakrabarti, D. Agarwal, and V. Josifovski. Contextual advertising by combining relevance with click feedback. In WWW, 2008. Google ScholarDigital Library
- A. Dey, G. Abowd, and D. Salber. A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human Computer Interaction, 2:97--166, 2001. Google ScholarDigital Library
- R. Hull, P. Neaves, and J. Bedford-Roberts. Toward situated computing. pages 146--153, 1997. Google ScholarDigital Library
- J. Indulska, R. Robinson, A. Rakotonirainy, K. Henricksen, M.-S. Chen, P. K. Chrysanthis, M. Sloman, and A. B. Zaslavsky. Experiences in using cc/pp in context-aware systems. Mobile Data Management,Lecture Notes in Computer Science, Springer, 2574, 2003. Google ScholarDigital Library
- J. Kiseleva, H. T. Lam, M. Pechenizkiy, and T. Calders. Discovery temporal hidden contexts in web sessions for user trail prediction. In TempWeb, 2013. Google ScholarDigital Library
- P. K. Novak, N. Lavrac, and G. I.Webb. Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining. Journal of Machine Learning Research (JMLR), 27:77--403, 2009. Google ScholarDigital Library
- C. Palmisano, A. Tuzhilin, and M. Gorgoglione. Using context to improve predictive modeling of customers in personalization applications. IEEE Transactions on Knowledge and Data Engineering (TKDE), 20(11):1535--1549, November 2008. Google ScholarDigital Library
- C. Prahalad. Predicts customer context is the next big thing. AMA MwWorld, 2004.Google Scholar
- S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt-Thieme. Fast context-aware recommendations with factorization machines. In SIGIR, volume 10, 2011. Google ScholarDigital Library
- D. Stern, R. Herbrich, and T. Graepel. Matchbox: Large scale online bayesian recommendations. In WWW, pages 111--120, 2009. Google ScholarDigital Library
- P. Turney. Exploiting context when learning to classify. 2002.Google Scholar
- P. Turney. The management of context-sensitive features: A review of strategies. In CoRR, 2002.Google Scholar
- B. Xiang, D. Jiang, J. Pei, X. Sun, E. Chen, and H. Li. Context-aware ranking in web search. In SIGIR, 2010. Google ScholarDigital Library
- E. Zliobaite, J. Bakker, and M. Pechenizkiy. Beating the baseline prediction in food sales: How intelligent an intelligent predictor is? Expert Syst. Appl. (ESWA), 39(1):806--815, 2012. Google ScholarDigital Library
- I. Zliobaite. Identifying hidden contexts in classification. In PAKDD (1), pages 277--288, 2011. Google ScholarDigital Library
Index Terms
- Context mining and integration into predictive web analytics
Recommendations
Using Context to Improve Predictive Modeling of Customers in Personalization Applications
The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much the contextual information really matters in building customer models in ...
Towards context-aware adaptable web services
WWW Alt. '04: Proceedings of the 13th international World Wide Web conference on Alternate track papers & postersIn this paper, we present a context framework that facilitates the development and deployment of context-aware adaptable Web services. Web services are provided with context information about clients that may be utilized to provide a personalized ...
A context type model for context-aware discovery of web services
ACST'07: Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and TechnologyA service registry such as UDDI is an important component for service discovery in an open Web services environment. Service discovery is refined if the registry is context-aware, i.e. realising the contexts of the querying service consumers as well as ...
Comments