Intelligent Systems for Tourism
IEEE Intelligent Systems
Fuzzy logic methods in recommender systems
Fuzzy Sets and Systems - Theme: Multicriteria decision
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
An Online Recommender System for Large Web Sites
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
IEEE Transactions on Knowledge and Data Engineering
An Improvement to Collaborative Filtering for Recommender Systems
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering
IEEE Intelligent Systems
Improving Accuracy of Recommender System by Clustering Items Based on Stability of User Similarity
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors
Interacting with Computers
Developing recommender systems with the consideration of product profitability for sellers
Information Sciences: an International Journal
Collaborative recommender systems: Combining effectiveness and efficiency
Expert Systems with Applications: An International Journal
Using Collaborative Filtering Algorithms as eLearning Tools
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
Community Collaborative Filtering for E-Learning
ICCEE '08 Proceedings of the 2008 International Conference on Computer and Electrical Engineering
Collaborative filtering adapted to recommender systems of e-learning
Knowledge-Based Systems
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
The effect of sparsity on collaborative filtering metrics
ADC '09 Proceedings of the Twentieth Australasian Conference on Australasian Database - Volume 92
Content-based recommendation in e-commerce
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part II
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
Location-based recommendation system using Bayesian user's preference model in mobile devices
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
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This paper presents the results obtained using a real e-learning recommender system where the collaborative filtering core has been adapted with the aim of weighting the importance of the recommendations in accordance with the users' knowledge. In this way, ratings from users with better knowledge of the given subject will have greater importance over ratings from users with less knowledge. In the same way, we validate the results obtained and we adjust, with just one parameter, the weight that should be awarded, in each specific e-learning recommender system, to the ratings of the users with the best reputation. The results obtained show a notable improvement regarding traditional collaborative filtering methods and suggest balanced weightings between the importance assigned to users with more or less knowledge.