Fırat Öncel

firat.oncel@mila.quebec

PhD - Concordia University

Supervisor

Mirco Ravanelli

Co-supervisor

Laurent Charlin

Research Topics

Audio Processing

Computer Vision

Deep Learning

Large Language Models (LLM)

Machine Learning For Speech and Audio

Recommender Systems

Representation Learning

Website

Google Scholar

GitHub

Publications

Controllable and Content-Based Recommendations

Traditional recommendation systems rely on latent (dense) representations, making them difficult to interpret and control. We propose the Co… (see more)ntrollable 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.

2026-07-22

arXiv (preprint)

doi.org

arxiv.org

Audio Prototypical Network For Controllable Music Recommendation

Traditional recommendation systems represent user preferences in dense representations obtained through black-box encoder models. While thes… (see more)e models often provide strong recommendation performance, they lack interpretability for users, leaving users unable to understand or control the system's modeling of their preferences. This limitation is especially challenging in music recommendation, where user preferences are highly personal and often evolve based on nuanced qualities like mood, genre, tempo, or instrumentation. In this paper, we propose an audio prototypical network for controllable music recommendation. This network expresses user preferences in terms of prototypes representative of semantically meaningful features pertaining to musical qualities. We show that the model obtains competitive recommendation performance compared to popular baseline models while also providing interpretable and controllable user profiles.

2024-12-31

MLSP (published)

doi.org

openreview.net

Adaptation Odyssey in LLMs: Why Does Additional Pretraining Sometimes Fail to Improve?

Fırat Öncel

Matthias Bethge

Beyza Ermis

Mirco Ravanaelli

Yusuf Cem Sübakan

cCaugatay Yildiz

2023-12-31