TY - GEN
T1 - Evaluating peer-to-peer recommender systems that exploit spontaneous affinities
AU - Ruffo, Giancarlo
AU - Schifanella, Rossano
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - Complex and social networks
KW - File sharing systems
KW - Peer-to-peer
KW - Recommender system
UR - http://www.scopus.com/inward/record.url?scp=35248821345&partnerID=8YFLogxK
U2 - 10.1145/1244002.1244338
DO - 10.1145/1244002.1244338
M3 - Conference contribution
AN - SCOPUS:35248821345
SN - 1595934804
SN - 9781595934802
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 1574
EP - 1578
BT - Proceedings of the 2007 ACM Symposium on Applied Computing
PB - Association for Computing Machinery
T2 - 2007 ACM Symposium on Applied Computing
Y2 - 11 March 2007 through 15 March 2007
ER -