178 citations · 466 across the 20 of their papers we have counts for
22 papers
Representation Online Matters: Practical End-to-End Diversification in Search and Recommender Systems
Pedro Silva, Bhawna Juneja, Shloka Desai +2
As the use of online platforms continues to grow across all demographics, users often express a desire to feel represented in the content. To improve representation in search resul…
Recommending Dream Jobs in a Biased Real World
Nadia Fawaz
Machine learning models learn what we teach them to learn. Machine learning is at the heart of recommender systems. If a machine learning model is trained on biased data, the resul…
A relevance-scalability-interpretability tradeoff with temporally evolving user personas
Snigdha Panigrahi, Nadia Fawaz
The current work characterizes the users of a VoD streaming space through user-personas based on a tenure timeline and temporal behavioral features in the absence of explicit user…
Sequential Relevance Maximization with Binary Feedback
Vijay Kamble, Nadia Fawaz, Fernando Silveira
Motivated by online settings where users can provide explicit feedback about the relevance of products that are sequentially presented to them, we look at the recommendation proces…
Managing your Private and Public Data: Bringing down Inference Attacks against your Privacy
Salman Salamatian, Amy Zhang, Flavio du Pin Calmon +5
We propose a practical methodology to protect a user's private data, when he wishes to publicly release data that is correlated with his private data, in the hope of getting some u…
Guess Who Rated This Movie: Identifying Users Through Subspace Clustering
Amy Zhang, Nadia Fawaz, Stratis Ioannidis +1
It is often the case that, within an online recommender system, multiple users share a common account. Can such shared accounts be identified solely on the basis of the userprovide…