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  1. Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec '11)
  2. Information market based recommender systems fusion
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Information market based recommender systems fusion
A kernel-based approach to exploiting interaction-networks in heterogeneous information sources for improved recommender systems
Learning multiple models for exploiting predictive heterogeneity in recommender systems
A generic semantic-based framework for cross-domain recommendation
Hybrid algorithms for recommending new items
Expert recommendation based on social drivers, social network analysis, and semantic data representation
Experience Discovery: hybrid recommendation of student activities using social network data
Personalizing tags: a folksonomy-like approach for recommending movies
Personalized pricing recommender system: multi-stage epsilon-greedy approach
Matrix co-factorization for recommendation with rich side information and implicit feedback

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Information market based recommender systems fusion

Content Provider ACM Digital Library
Author Mentzas, Gregoris Christidis, Konstantinos Bothos, Efthimios Apostolou, Dimitris
Abstract Recommender Systems have emerged as a way to tackle the overload of information reflected 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 Netflix datasets and discuss the results of our experiments.
Starting Page 1
Ending Page 8
Page Count 8
File Format PDF
ISBN 9781450310277
DOI 10.1145/2039320.2039321
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2011-10-27
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Information markets Collaborative filtering Content-based recommendation Ensemble recommenders
Content Type Text
Resource Type Article
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