MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
IIS '09 Proceedings of the 2009 International Conference on Industrial and Information Systems
An Item-based Collaborative Filtering Recommendation Algorithm Using Slope One Scheme Smoothing
ISECS '09 Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02
Modern Information Retrieval
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Rank and relevance in novelty and diversity metrics for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Pareto-efficient hybridization for multi-objective recommender systems
Proceedings of the sixth ACM conference on Recommender systems
Who likes it more?: mining worth-recommending items from long tails by modeling relative preference
Proceedings of the 7th ACM international conference on Web search and data mining
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Recommender systems are quickly becoming ubiquitous in many Web applications, including e-commerce, social media channels, content providers, among others. These systems act as an enabling mechanism designed to overcome the information overload problem by improving browsing and consumption experience. Crucial to the performance of a recommender system is the accuracy of the user profiles used to represent the interests of the users. In this proposal, we analyze three different aspects of user profiling: (i) selecting the most informative events from the interaction between users and the system, (ii) combining different recommendation algorithms to (iii) including trust-aware information in user profiles to improve the accuracy of recommender systems.