EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems
Proceedings of the third ACM conference on Recommender systems
Proceedings of the fourth ACM conference on Recommender systems
A recommender system based on tag and time information for social tagging systems
Expert Systems with Applications: An International Journal
Context relevance assessment for recommender systems
Proceedings of the 16th international conference on Intelligent user interfaces
A case study in a recommender system based on purchase data
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Incorporating context into recommender systems: an empirical comparison of context-based approaches
Electronic Commerce Research
Using context to improve the effectiveness of segmentation and targeting in e-commerce
Expert Systems with Applications: An International Journal
Customer relationship management and Web mining: the next frontier
Data Mining and Knowledge Discovery
Proceedings of the 6th Euro American Conference on Telematics and Information Systems
Context relevance assessment and exploitation in mobile recommender systems
Personal and Ubiquitous Computing
Contextual recommendations for groups
ER'12 Proceedings of the 2012 international conference on Advances in Conceptual Modeling
Fast group recommendations by applying user clustering
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
Trust beyond reputation: A computational trust model based on stereotypes
Electronic Commerce Research and Applications
Cluster searching strategies for collaborative recommendation systems
Information Processing and Management: an International Journal
Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems
Information Processing and Management: an International Journal
Context mining and integration into predictive web analytics
Proceedings of the 22nd international conference on World Wide Web companion
Discovering temporal hidden contexts in web sessions for user trail prediction
Proceedings of the 22nd international conference on World Wide Web companion
SoCo: a social network aided context-aware recommender system
Proceedings of the 22nd international conference on World Wide Web
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
Hybreed: A software framework for developing context-aware hybrid recommender systems
User Modeling and User-Adapted Interaction
Comparing context-aware recommender systems in terms of accuracy and diversity
User Modeling and User-Adapted Interaction
YmalDB: exploring relational databases via result-driven recommendations
The VLDB Journal — The International Journal on Very Large Data Bases
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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 have 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 the paper. These findings have significant implications for data miners and marketers. They show that contextual information does matter in personalization applications 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.