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cs.IR2025
APS Explorer: Navigating Algorithm Performance Spaces for Informed Dataset Selection
Tobias Vente, Michael Heep, Abdullah Abbas +3
Dataset selection is crucial for offline recommender system experiments, as mismatched data (e.g., sparse interaction scenarios require datasets with low user-item density) can lea…
cs.IR2025
Discrete-event Tensor Factorization: Learning a Smooth Embedding for Continuous Domains
Joey De Pauw, Bart Goethals
Recommender systems learn from past user behavior to predict future user preferences. Intuitively, it has been established that the most recent interactions are more indicative of…
cs.IR2025
Weighted Tensor Decompositions for Context-aware Collaborative Filtering
Joey De Pauw, Bart Goethals
Over recent years it has become well accepted that user interest is not static or immutable. There are a variety of contextual factors, such as time of day, the weather or the user…