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cs.IR2026
Give Users the Wheel: Towards Promptable Recommendation Paradigm
Fuyuan Lyu, Chenglin Luo, Qiyuan Zhang +6
Conventional sequential recommendation models have achieved remarkable success in mining implicit behavioral patterns. However, these architectures remain structurally blind to exp…
cs.IR2025
Audio Prototypical Network For Controllable Music Recommendation
Fırat Öncel, Emiliano Penaloza, Haolun Wu +4
Traditional recommendation systems represent user preferences in dense representations obtained through black-box encoder models. While these models often provide strong recommenda…
cs.IR2024
TEARS: Textual Representations for Scrutable Recommendations
Emiliano Penaloza, Olivier Gouvert, Haolun Wu +1
Traditional recommender systems rely on high-dimensional (latent) embeddings for modeling user-item interactions, often resulting in opaque representations that lack interpretabili…