RecSys 2021 challenge workshop: Fairness-aware engagement prediction at scale on Twiter's Home Timeline

Vito Walter Anelli, Saikishore Kalloori, Bruce Ferwerda, Luca Belli, Alykhan Tejani, Frank Portman, Alexandre Lung-Yut-Fong, Ben Chamberlain, Yuanpu Xie, Jonathan Hunt, Michael Bronstein, Wenzhe Shi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The workshop features presentations of accepted contributions to the RecSys Challenge 2021, organized by Politecnico di Bari, ETH Zürich, Jönköping University, and the data set is 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 also increases the data size to encourage novel methods. Also, the data density is 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. In fact, multi-goal optimization considering accuracy and fairness is particularly challenging. However, we believed that the recommendation community was nowadays mature enough to face the challenge of providing accurate and, at the same time, fair recommendations. To this end, Twitter has released a public dataset of close to 1 billion data points, > 40 million each day over 28 days. Week 1-3 will be used for training and week 4 for evaluation and testing. Each datapoint contains the tweet along with engagement features, user features, and tweet features. A peculiarity of this challenge is related to keeping the dataset updated with the platform: if a user deletes a Tweet, or their data from Twitter, the dataset is promptly updated. Moreover, each change in the dataset implied new evaluations of all submissions and the update of the leaderboard metrics. The challenge was well received with 578 registered users, and 386 submissions.

Original languageEnglish
Title of host publicationRecSys 2021 - 15th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages819-824
Number of pages6
ISBN (Electronic)9781450384582
DOIs
StatePublished - 13 Sep 2021
Externally publishedYes
Event15th ACM Conference on Recommender Systems, RecSys 2021 - Virtual, Online, Netherlands
Duration: 27 Sep 20211 Oct 2021

Publication series

NameRecSys 2021 - 15th ACM Conference on Recommender Systems

Conference

Conference15th ACM Conference on Recommender Systems, RecSys 2021
Country/TerritoryNetherlands
CityVirtual, Online
Period27/09/211/10/21

Keywords

  • BERT
  • Embeddings
  • Fairness
  • Online Social Networks
  • Recommender Systems

Fingerprint

Dive into the research topics of 'RecSys 2021 challenge workshop: Fairness-aware engagement prediction at scale on Twiter's Home Timeline'. Together they form a unique fingerprint.

Cite this