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Using Context to Improve Predictive Modeling of Customers in Personalization Applications

Authors :
Alexander Tuzhilin
Michele Gorgoglione
Cosimo Palmisano
Source :
IEEE Transactions on Knowledge and Data Engineering. 20:1535-1549
Publication Year :
2008
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2008.

Abstract

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 personalization applications has been done before. In this paper, we study how important the contextual information is when predicting customer behavior and how to use it when building customer models. It is done by conducting an empirical study across a wide range of experimental conditions. The experimental results show that context does matter when modeling the behavior of individual customers and that it is possible to infer the context from the existing data with reasonable accuracy in certain cases. It is also shown that significant performance improvements can be achieved if the context is "cleverly" modeled, as described in this paper. These findings have significant implications for data miners and marketers. They show that contextual information does matter in personalization and companies have different opportunities to both make context valuable for improving predictive performance of customers' behavior and decreasing the costs of gathering contextual information.

Details

ISSN :
10414347
Volume :
20
Database :
OpenAIRE
Journal :
IEEE Transactions on Knowledge and Data Engineering
Accession number :
edsair.doi.dedup.....6ab1034a5ef70d599b49db737584a32c
Full Text :
https://doi.org/10.1109/tkde.2008.110