C4.5: programs for machine learning
C4.5: programs for machine learning
Meta-recommendation systems: user-controlled integration of diverse recommendations
Proceedings of the eleventh international conference on Information and knowledge management
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Proceedings of the 10th international conference on Intelligent user interfaces
CubeSVD: a novel approach to personalized Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Using ODP metadata to personalize search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Detecting noise in recommender system databases
Proceedings of the 11th international conference on Intelligent user interfaces
SearchTogether: an interface for collaborative web search
Proceedings of the 20th annual ACM symposium on User interface software and technology
Collaborative information seeking: A field study of a multidisciplinary patient care team
Information Processing and Management: an International Journal
A survey of collaborative web search practices
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A recommender system with interest-drifting
WISE'07 Proceedings of the 8th international conference on Web information systems engineering
Semantic service matchmaking for Digital Health Ecosystems
Knowledge-Based Systems
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
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The so-called Social Web has helped to change the very nature of the Internet by emphasising the role of our online experiences as new forms of content and service knowledge. In this paper we describe an approach to improving mainstream Web search by harnessing the search experiences of groups of like-minded searchers. We focus on the HeyStaks system (www.heystaks.com) and look in particular at the experiential knowledge that drives its search recommendations. Specifically we describe how this knowledge can be noisy, and we describe and evaluate a recommendation technique for coping with this noise and discuss how it may be incorporated into HeyStaks as a useful feature.