Communications of the ACM
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
Data Mining and Knowledge Discovery
Privacy Risks in Recommender Systems
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Proceedings of the thirteenth ACM international conference on Information and knowledge management
IEEE Transactions on Knowledge and Data Engineering
Content-based music filtering system with editable user profile
Proceedings of the 2006 ACM symposium on Applied computing
A user-oriented contents recommendation system in peer-to-peer architecture
Expert Systems with Applications: An International Journal
IEEE Transactions on Knowledge and Data Engineering
Generating semantically enriched user profiles for Web personalization
ACM Transactions on Internet Technology (TOIT)
A hybrid approach for movie recommendation
Multimedia Tools and Applications
Addressing cold-start problem in recommendation systems
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Intelligent Multimedia Recommender by Integrating Annotation and Association Mining
SUTC '08 Proceedings of the 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (sutc 2008)
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluation of an ontology-content based filtering method for a personalized newspaper
Proceedings of the 2008 ACM conference on Recommender systems
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
Lire: lucene image retrieval: an extensible java CBIR library
MM '08 Proceedings of the 16th ACM international conference on Multimedia
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Personalized Recommendation over a Customer Network for Ubiquitous Shopping
IEEE Transactions on Services Computing
Mining Indirect Association Rules for Web Recommendation
International Journal of Applied Mathematics and Computer Science
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Collaborative filtering recommender systems
The adaptive web
Content-based recommendation systems
The adaptive web
A ranking method for multimedia recommenders
Proceedings of the ACM International Conference on Image and Video Retrieval
A multimedia recommender integrating object features and user behavior
Multimedia Tools and Applications
On the stability of recommendation algorithms
Proceedings of the fourth ACM conference on Recommender systems
Proceedings of the fourth ACM conference on Recommender systems
A Recommender System for Youtube Based on its Network of Reviewers
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
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The extraordinary technological progress we have witnessed in recent years has made it possible to generate and exchange multimedia content at an unprecedented rate. As a consequence, massive collections of multimedia objects are now widely available to a large population of users. As the task of browsing such large collections could be daunting, Recommender Systems are being developed to assist users in finding items that match their needs and preferences. In this article, we present a novel approach to recommendation in multimedia browsing systems, based on modeling recommendation as a social choice problem. In social choice theory, a set of voters is called to rank a set of alternatives, and individual rankings are aggregated into a global ranking. In our formulation, the set of voters and the set of alternatives both coincide with the set of objects in the data collection. We first define what constitutes a choice in the browsing domain and then define a mechanism to aggregate individual choices into a global ranking. The result is a framework for computing customized recommendations by originally combining intrinsic features of multimedia objects, past behavior of individual users, and overall behavior of the entire community of users. Recommendations are ranked using an importance ranking algorithm that resembles the well-known PageRank strategy. Experiments conducted on a prototype of the proposed system confirm the effectiveness and efficiency of our approach.