M. de Rijke

UvA-DARE (Digital

UvA-DARE (Digital

LSNR: Learned Sparse News Recommendations with Attentive User Modeling

LSNR encodes each article independently and constructs a sparse user vector by combining learned, attention-based weighting of the per-article sparse representations with a dense-projected expansion at the user level, which confirms that learned attention-based aggregation outperforms simpler pooling alternatives.

Prior-Data Fitted Networks as Tabular Foundation Models for Ranking in Low-Data Settings

This work demonstrates that PFNs, which are originally trained for classification, successfully outperform classification baselines on ranking data and introduces a novel sampling and inference scheme to obtain pairwise predictions from PFNs' native pointwise architecture, analogous to pairwise LTR.