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
Peer-to-peer based recommendations for mobile commerce
WMC '01 Proceedings of the 1st international workshop on Mobile commerce
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
A decentralized CF approach based on cooperative agents
Proceedings of the 15th international conference on World Wide Web
A peer-to-peer recommender system based on spontaneous affinities
ACM Transactions on Internet Technology (TOIT)
On the value of random opinions in decentralized recommendation
DAIS'06 Proceedings of the 6th IFIP WG 6.1 international conference on Distributed Applications and Interoperable Systems
Bridging the gap: complex networks meet information and knowledge management
Proceedings of the 18th ACM conference on Information and knowledge management
Overlay management for fully distributed user-based collaborative filtering
EuroPar'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part I
Asynchronous peer-to-peer data mining with stochastic gradient descent
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Proceedings of the Fifth Balkan Conference in Informatics
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Traditionally, collaborative filtering (CF) algorithms used for recommendation operate on complete knowledge. This makes these algorithms hard to employ in a decentralized context where not all users' ratings can be available at all locations. In this paper we investigate how the well-known neighbourhood-based CF algorithm by Herlocker et al. operates on partial knowledge; that is, how many similar users does the algorithm actually need to produce good recommendations for a given user, and how similar must those users be. We show for the popular MovieLens 1,000,000 and Jester datasets that sufficiently good recommendations can be made based on the ratings of a neighbourhood consisting of a relatively small number of randomly selected users.