Selecting Examples for Partial Memory Learning
Machine Learning
A graph-based recommender system for digital library
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Personalised hypermedia presentation techniques for improving online customer relationships
The Knowledge Engineering Review
IEEE Transactions on Knowledge and Data Engineering
Journal of Systems and Software
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Recency-based collaborative filtering
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
Provision of distance learning services over Interactive Digital TV with MHP
Computers & Education
The value of personalised recommender systems to e-business: a case study
Proceedings of the 2008 ACM conference on Recommender systems
Journal of Systems and Software
Analysis of Variance Designs: A Conceptual and Computational Approach with SPSS and SAS
Analysis of Variance Designs: A Conceptual and Computational Approach with SPSS and SAS
An empirical study on effectiveness of temporal information as implicit ratings
Expert Systems with Applications: An International Journal
Personalizing e-Commerce by Semantics-Enhanced Strategies and Time-Aware Recommendations
SMAP '08 Proceedings of the 2008 Third International Workshop on Semantic Media Adaptation and Personalization
Dynamic micro-targeting: fitness-based approach to predicting individual preferences
Knowledge and Information Systems
Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
A personalized recommendation system based on product taxonomy for one-to-one marketing online
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
MiSPOT: dynamic product placement for digital TV through MPEG-4 processing and semantic reasoning
Knowledge and Information Systems
Exploiting digital TV users' preferences in a tourism recommender system based on semantic reasoning
IEEE Transactions on Consumer Electronics
RESYGEN: A Recommendation System Generator using domain-based heuristics
Expert Systems with Applications: An International Journal
Student progress assessment with the help of an intelligent pupil analysis system
Engineering Applications of Artificial Intelligence
SigTur/E-Destination: Ontology-based personalized recommendation of Tourism and Leisure Activities
Engineering Applications of Artificial Intelligence
Improving recommendation performance through ontology-based semantic similarity
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Collective intelligence as mechanism of medical diagnosis: The iPixel approach
Expert Systems with Applications: An International Journal
Recommender System to Analyze Student's Academic Performance
Expert Systems with Applications: An International Journal
Transfer learning of syntactic structures for building taxonomies for search engines
Engineering Applications of Artificial Intelligence
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Recommender systems in online shopping automatically select the most appropriate items to each user, thus shortening his/her product searching time in the shops and adapting the selection as his/her particular preferences evolve over time. This adaptation process typically considers that a user's interest in a given type of product always decreases with time from the moment of the last purchase. However, the necessity of a product for a user depends on both the nature of the own item and the personal preferences of the user, being even possible that his/her interest increases over time from the purchase. Some existing approaches focus only on the first factor, missing the point that the influence of time can be very different for different users. To solve this limitation, we present a filtering strategy that exploits the semantics formalized in an ontology in order to link items (and their features) to time functions. The novelty lies within the fact that the shapes of these functions are corrected by temporal curves built from the consumption stereotypes into which each user fits best. Our preliminary experiments involving real users have revealed significant improvements of recommendation precision with regard to previous time-driven filtering approaches.