Machine Learning
Learning implicit user interest hierarchy for context in personalization
Proceedings of the 8th international conference on Intelligent user interfaces
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
Enriching Information Agents' Knowledge by Ontology Comparison: A Case Study
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Formal Analysis of Models for the Dynamics of Trust Based on Experiences
MAAMAW '99 Proceedings of the 9th European Workshop on Modelling Autonomous Agents in a Multi-Agent World: MultiAgent System Engineering
Ubiquitous User Modeling for Situated Interaction
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Opinion-Based Filtering through Trust
CIA '02 Proceedings of the 6th International Workshop on Cooperative Information Agents VI
Building and applying a concept hierarchy representation of a user profile
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
User Profiling for Web Page Filtering
IEEE Internet Computing
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Mining Ontology for Automatically Acquiring Web User Information Needs
IEEE Transactions on Knowledge and Data Engineering
Improving collaborative filtering with trust-based metrics
Proceedings of the 2006 ACM symposium on Applied computing
Investigating interactions of trust and interest similarity
Decision Support Systems
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
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
Trust no one: evaluating trust-based filtering for recommenders
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Trust network inference for online rating data using generative models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
Mining Divergent Opinion Trust Networks through Latent Dirichlet Allocation
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Trust-based authentication scheme with user rating for low-resource devices in smart environments
Personal and Ubiquitous Computing
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As a consequence of the exponential growth of Internet and its services, including social applications fostering collaboration on the Web, information sharing had become pervasive. This caused a crescent need of more powerful tools to help users with the task of selecting interesting resources. Recommender systems have emerged as a solution to evaluate the quality of massively user-generated contents in open environments and provide recommendations based not only on the user interests but also on the opinions of people with similar tastes. In addition to interest similarity, however, trustworthiness is a factor that recommenders have to consider in the selection of reliable peers for collaboration. Most approaches in this regard estimates trust base on global user profile similarity or history of exchanged opinions. In this paper, we propose a novel approach for agent-based recommendation in which trust is independently learned and evolved for each pair of interest topics two users have in common. Experimental results show that agents learning who to trust about certain topics reach better levels of precision than considering interest similarity exclusively.