Communications of the ACM
Peer-to-peer based recommendations for mobile commerce
WMC '01 Proceedings of the 1st international workshop on Mobile commerce
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A market-based approach to recommender systems
ACM Transactions on Information Systems (TOIS)
Distributed collaborative filtering for peer-to-peer file sharing systems
Proceedings of the 2006 ACM symposium on Applied computing
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A decentralized recommendation system based on self-organizing partnerships
NETWORKING'06 Proceedings of the 5th international IFIP-TC6 conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications Systems
P2P systems in legal networks: another
Proceedings of the 11th international conference on Artificial intelligence and law
MobHinter: epidemic collaborative filtering and self-organization in mobile ad-hoc networks
Proceedings of the 2008 ACM conference on Recommender systems
A peer-to-peer recommender system based on spontaneous affinities
ACM Transactions on Internet Technology (TOIT)
Design of a P2P content recommendation system using affinity networks
Computer Communications
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The validation of a recommender system is always a quite hazardous task, because of the difficulty of modeling the tastes of a given user. Novel (decentralized) recommender systems are proposed and evaluated by way of well known logs of user profiles and buddy tables, that contain lists of items with feedback ratings assigned by a given set of users. These information are cross linked, and the precision of the recommendation is compared with other well known (centralized) systems. This evaluation approach cannot be applied in the actual peer-to-peer domain: it is difficult, if not impossible, to build and maintain user profiles, and users are not required to give feedbacks to a data collector entity. Moreover, objects are poorly or not structured, and meta-information, when present, cannot be trusted because of fake files and incomplete item descriptions. In this paper, we present an evaluation process based on a 10-fold cross validation task, that we applied to estimate accuracy of the suggestions of a P2P recommender system recently proposed in [2]. The complexity of the evaluation of this peculiar recommender is increased because of "spontaneous affinities" between users that are used instead of classical knowledge representation based strategies.