RecSys 2021 Challenge Workshop: Fairness-aware engagement prediction at scale on Twitter’s Home Timeline (Hybrid)
RecSys 2021 Challenge Workshop: Fairness-aware engagement prediction at scale on Twitter’s Home Timeline
Vito Walter Anelli, Politecnico di Bari | Saikishore Kalloori, ETH Zürich | Bruce Ferwerda, Jönköping University | Luca Belli, Twitter Inc. | Alykhan Tejani, Twitter Inc. | Frank Portman, Twitter Inc. | Alexandre Lung-Yut-Fong, Twitter Inc. | Ben Chamberlain, Twitter Inc. | Yuanpu Xie, Twitter Inc. | Jonathan Hunt, Twitter Inc. | Michael Bronstein, Twitter Inc. | Wenzhe Shi, Twitter Inc.
The RecSys Challenge 2021 will be organized by Politecnico di Bari, ETH Zürich, Jönköping University, and the data set will be provided by Twitter. The challenge focuses on a real-world task of tweet engagement prediction in a dynamic environment. For 2021, the challenge considers four different engagement types: Likes, Retweet, Quote, and replies. This year’s challenge brings the problem even closer to Twitter’s real recommender systems by introducing latency constraints. We will also increase the data size to encourage novel methods. Also, the data density will be increased in terms of the graph where users are considered to be nodes and interactions as edges.The goal is twofold: to predict the probability of different engagement types of a target user for a set of tweets based on heterogeneous input data while providing fair recommendations. We are conscious that multi-goal optimization considering accuracy and fairness will be particularly challenging. However, we believe that the recommendation community is nowadays mature enough to face the challenge of providing accurate and, at the same time, fair recommendations.A detailed description of the challenge is available on the website of the RecSys Challenge 2021.Accepted contributions will be presented during the RecSys Challenge 2021 Workshop.