Work

Controllable and Content-Based Recommendations

Year: 2026

Type: preprint

Abstract: Traditional recommendation systems rely on latent (dense) representations, making them difficult to interpret and control. We propose the Controllable and Content-Based Recommendations (CCBR) framework, which builds its recommendations from textual user profile representations. CCBR plugs into collaborative filtering models and introduces controllability via text bottlenecks. We show that CCBR enables text-based and multimodal interventions, allowing users to steer the model towards the directions they prefer. Different from existing controllable recommendation systems, CCBR infers the text summaries directly from item contents (images, audio or video). Across image-, audio-, and video-based datasets, we demonstrate that the proposed framework obtains competitive model performance with standard (latent-representation) models while providing controllable model summaries via text. The model also outperforms TEARS, a recent baseline for controllable recommendation systems. Through systematic interventions, we demonstrate the efficacy of the user steering mechanism.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2607.20938

Authors: Öncel, Fırat, Jeong, Jihoon, Penaloza, Emiliano, Ravanelli, Mirco, Charlin, Laurent, Subakan, Cem

Institutions: Mila - Quebec Artificial Intelligence Institute, Concordia University, Université Laval, Université de Montréal, HEC Montréal

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7170957697

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