Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
Recommendation with Differential Context Weighting
verfasst von : Yong Zheng, Robin Burke, Bamshad Mobasher
Erschienen in: User Modeling, Adaptation, and Personalization
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Context-aware recommender systems (CARS) adapt their recommendations to users’ specific situations. In many recommender systems, particularly those based on collaborative filtering, the contextual constraints may lead to sparsity: fewer matches between the current user context and previous situations. Our earlier work proposed an approach called
differential context relaxation
(DCR), in which different subsets of contextual features were applied in different components of a recommendation algorithm. In this paper, we expand on our previous work on DCR, proposing a more general approach –
differential context weighting
(DCW), in which contextual features are weighted. We compare DCR and DCW on two real-world datasets, and DCW demonstrates improved accuracy over DCR with comparable coverage. We also show that particle swarm optimization (PSO) can be used to efficiently determine the weights for DCW.