Collaborative Classification from Noisy Labels


We consider a setting where users interact with a collection of N items on an online platform. We are given class labels possibly corrupted by noise, and we seek to recover the true class of each item. We postulate a simple probabilistic model of the interactions between users and items, based on the assumption that users interact with classes in different proportions. We then develop a message-passing algorithm that decodes the noisy class labels efficiently. Under suitable assumptions, our method provably recovers all items’ true classes in the large N limit, even when the interaction graph remains sparse. Empirically, we show that our approach is effective on several practical applications, including predicting the location of businesses, the category of consumer goods, and the language of audio content.


April 2022 | The Web Conference (WWW)

Mostra: A Flexible Balancing Framework to Trade-off User, Artist and Platform Objectives for Music Sequencing

Emanuele Bugliarello, Rishabh Mehrotra, James Kirk, Mounia Lalmas

February 2022 | WSDM

Variational User Modeling with Slow and Fast Features

Ghazal Fazelnia, Eric Simon, Ian Anderson, Ben Carterette, Mounia Lalmas

November 2021 | ISMIR - International Society for Music Information Retrieval Conference

Multi-Task Learning of Graph-based Inductive Representations of Music Content

Antonia Saravanou, Federico Tomasi, Rishabh Mehrotra and Mounia Lalmas