Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
Referral Web: combining social networks and collaborative filtering
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Fast discovery of association rules
Advances in knowledge discovery and data mining
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Textual data mining of service center call records
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
IEEE Transactions on Knowledge and Data Engineering
Modeling user's opinion relevance to recommending research papers
UM'05 Proceedings of the 10th international conference on User Modeling
User Modeling for Recommendation in Blogspace
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Research on the Recommending Method Used in C2C Online Trading
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
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Considerable time is wasted in searching for information on the Internet because there is such a variety of items competing for attention. In an attempt to minimize this difficulty, and to provide support in the search for interesting and useful information, significant research efforts have been made --- from systems based on pure information recovery, to systems applying information filtering to recommend items. In this paper we describe a model (Mo-DROP) for the computation of the user's relevance of opinion (Recommneder's Rank metric) and its application in a specific domain of knowledge using information from Recommender Systems. The aim of this solution is to offer the users of a Recommender System conditions to identify other people's authority in a Collaborative Filtering mechanism. In addition, we present an extended example and an experiment applying this model and the Recommender's Rank metric.