Information market based recommender systems fusion

Bothos, E; Christidis, K; Apostolou, D; Mentzas, G

dc.contributor.author	Bothos, E	en
dc.contributor.author	Christidis, K	en
dc.contributor.author	Apostolou, D	en
dc.contributor.author	Mentzas, G	en
dc.date.accessioned	2014-03-01T02:53:19Z
dc.date.available	2014-03-01T02:53:19Z
dc.date.issued	2011	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36233
dc.subject	Collaborative filtering	en
dc.subject	Content-based recommendation	en
dc.subject	Ensemble recommenders	en
dc.subject	Information markets	en
dc.subject.other	Building blockes	en
dc.subject.other	Collaborative filtering	en
dc.subject.other	Content-based recommendation	en
dc.subject.other	Data sets	en
dc.subject.other	Ensemble recommenders	en
dc.subject.other	Information market	en
dc.subject.other	User activity	en
dc.subject.other	Blending	en
dc.subject.other	Commerce	en
dc.subject.other	Data processing	en
dc.subject.other	Recommender systems	en
dc.title	Information market based recommender systems fusion	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1145/2039320.2039321	en
heal.identifier.secondary	http://dx.doi.org/10.1145/2039320.2039321	en
heal.publicationDate	2011	en
heal.abstract	Recommender Systems have emerged as a way to tackle the overload of information reected in the increasing volume of information artefacts in the web and elsewhere. Recommender Systems analyse existing information on the user activities in order to estimate future preferences. However, in real life situations, different types of information can be found and their interpretation can vary as well. Each recommender system implements a different approach for utilizing the known information and predicting the user preferences. A problem is that of blending the recommendations in an adaptive, intuitive way while performing better than base recommenders. In this work we propose an approach based on information markets for the fusion of recommender systems. Information Markets have unique characteristics that make them suitable building blocks for ensemble recommenders. We evaluate our approach with the Movielens and Netix datasets and discuss the results of our experiments. Copyright © 2011 ACM.	en
heal.journalName	Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec 2011 - Held at the 5th ACM Conference on Recommender Systems, RecSys 2011	en
dc.identifier.doi	10.1145/2039320.2039321	en
dc.identifier.spage	1	en
dc.identifier.epage	8	en