activity
20182023
most citedWhen the Music Stops: Tip-of-the-Tongue Retrieval for Music

5 citations · 11 across the 4 of their papers we have counts for

collaborators

5 papers

cs.IR20235 cited

When the Music Stops: Tip-of-the-Tongue Retrieval for Music

Samarth Bhargav, Anne Schuth, Claudia Hauff

We present a study of Tip-of-the-tongue (ToT) retrieval for music, where a searcher is trying to find an existing music entity, but is unable to succeed as they cannot accurately r…

cs.IR20233 cited

Market-Aware Models for Efficient Cross-Market Recommendation

Samarth Bhargav, Mohammad Aliannejadi, Evangelos Kanoulas

We consider the cross-market recommendation (CMR) task, which involves recommendation in a low-resource target market using data from a richer, auxiliary source market. Prior work…

cs.AI20213 cited

Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence

Ana Lucic, Maurits Bleeker, Sami Jullien +2

In this work, we explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence (FACT-AI) at the…

cs.IR2021

Controllable Recommenders using Deep Generative Models and Disentanglement

Samarth Bhargav, Evangelos Kanoulas

In this paper, we consider controllability as a means to satisfy dynamic preferences of users, enabling them to control recommendations such that their current preference is met. W…

cs.LG2018

Sinkhorn AutoEncoders

Giorgio Patrini, Rianne van den Berg, Patrick Forré +5

Optimal transport offers an alternative to maximum likelihood for learning generative autoencoding models. We show that minimizing the p-Wasserstein distance between the generator…