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
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
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Self-similarity in World Wide Web traffic: evidence and possible causes
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On power-law relationships of the Internet topology
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Small worlds: the dynamics of networks between order and randomness
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A Framework for Collaborative, Content-Based and Demographic Filtering
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Peer-to-peer based recommendations for mobile commerce
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A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
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WIAPP '03 Proceedings of the The Third IEEE Workshop on Internet Applications
Webcrawler: finding what people want
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Self-organizing distributed collaborative filtering
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ACM Transactions on Information Systems (TOIS)
Linked: How Everything Is Connected to Everything Else and What It Means
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Evaluating peer-to-peer recommender systems that exploit spontaneous affinities
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Collaborative filtering using random neighbours in peer-to-peer networks
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A hybrid collaborative filtering recommendation mechanism for P2P networks
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Knowledge-Based Systems
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Network analysis has proved to be very useful in many social and natural sciences, and in particular Small World topologies have been exploited in many application fields. In this article, we focus on P2P file sharing applications, where spontaneous communities of users are studied and analyzed. We define a family of structures that we call “Affinity Networks” (or even Graphs) that show self-organized interest-based clusters. Empirical evidence proves that affinity networks are small worlds and shows scale-free features. The relevance of this finding is augmented with the introduction of a proactive recommendation scheme, namely DeHinter, that exploits this natural feature. The intuition behind this scheme is that a user would trust her network of “elective affinities” more than anonymous and generic suggestions made by impersonal entities. The accuracy of the recommendation is evaluated by way of a 10-fold cross validation, and a prototype has been implemented for further feedbacks from the users.