Journal of the American Society for Information Science - Special issue: relevance research
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Communications of the ACM
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
Compound Critiques for Conversational Recommender Systems
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Proceedings of the 10th international conference on Intelligent user interfaces
Exploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine
User Modeling and User-Adapted Interaction
Towards a formalization of value-centric trust in agent societies
Web Intelligence and Agent Systems
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A study of selection noise in collaborative web search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Proceedings of the 12th international conference on Intelligent user interfaces
Resource collaboration system based on dynamic user preference and context
Artificial Intelligence Review
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Recommender systems combine ideas from information retrieval, user modelling, and artificial intelligence to focus on the provision of more intelligent and proactive information services. As such, recommender systems play an important role when it comes to assisting the user during both routine and specialised information retrieval tasks. Like any good assistant it is important that users can trust in the ability of a recommender system to respond with timely and relevant suggestions. In this paper, we will look at a collaborative recommendation system operating in the domain of Web search. We will show how explicit models of trust can help to inform more reliable recommendations that translate into more relevant search results. Moreover, we demonstrate how the availability of this trust-model facilitates important interface enhancements that provide a means to declare the provenance of result recommendations in a way that will allow searchers to evaluate their likely relevance based on the reputation and trustworthiness of the recommendation partners behind these suggestions.