no code implementations • 18 Sep 2018 • Dan Shiebler, Luca Belli, Jay Baxter, Hanchen Xiong, Abhishek Tayal
Every day, hundreds of millions of new Tweets containing over 40 languages of ever-shifting vernacular flow through Twitter.
no code implementations • 28 Apr 2020 • Luca Belli, Sofia Ira Ktena, Alykhan Tejani, Alexandre Lung-Yut-Fon, Frank Portman, Xiao Zhu, Yuanpu Xie, Akshay Gupta, Michael Bronstein, Amra Delić, Gabriele Sottocornola, Walter Anelli, Nazareno Andrade, Jessie Smith, Wenzhe Shi
Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives.
1 code implementation • 8 Aug 2020 • Shubhanshu Mishra, Sijun He, Luca Belli
Named Entity Recognition (NER) is often the first step towards automated Knowledge Base (KB) generation from raw text.
no code implementations • 21 Aug 2020 • Smitha Milli, Luca Belli, Moritz Hardt
Most recommendation engines today are based on predicting user engagement, e. g. predicting whether a user will click on an item or not.
no code implementations • 19 Jul 2021 • Smitha Milli, Luca Belli, Moritz Hardt
Our results suggest that observational studies derived from user self-selection are a poor alternative to randomized experimentation on online platforms.
no code implementations • 3 Feb 2022 • Tomo Lazovich, Luca Belli, Aaron Gonzales, Amanda Bower, Uthaipon Tantipongpipat, Kristian Lum, Ferenc Huszar, Rumman Chowdhury
We show that we can use these metrics to identify content suggestion algorithms that contribute more strongly to skewed outcomes between users.
no code implementations • 12 Sep 2022 • Amanda Bower, Kristian Lum, Tomo Lazovich, Kyra Yee, Luca Belli
Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user.
no code implementations • 7 Oct 2022 • Kyra Yee, Alice Schoenauer Sebag, Olivia Redfield, Emily Sheng, Matthias Eck, Luca Belli
Harmful content detection models tend to have higher false positive rates for content from marginalized groups.