Name
Abstract | ||
|---|---|---|
ABSTRACT The workshop features presentations of accepted contributions to the RecSys Challenge 2020, organized by Politecnico di Bari, Free University of Bozen-Bolzano, TU Wien, University of Colorado, Boulder, and Universidade Federal de Campina Grande, and sponsored by Twitter. The challenge focuses on a real-world task of Tweet engagement prediction in a dynamic environment. The goal is to predict the probability for different types of engagement (Like, Reply, Retweet, and Retweet with comment) of a target user for a set of Tweets, based on heterogeneous input data. To this end, Twitter has released a large public dataset of ~160M public Tweets, obtained by subsampling within ~2 weeks, that contains engagement features, user features, and Tweet features. A peculiarity of this challenge is related to the recent regulations on data protection and privacy. The challenge data set was compliant: if a user deleted a Tweet, or their data from Twitter, the dataset was 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 1,131 registered users. In the final phase, 20 teams were competing for the winning position. These teams had an average size of approximately 4 participants and developed an overall number of 127 different methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3383313.3411532 | RECSYS |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 15 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vito Walter Anelli | 1 | 91 | 18.45 |
| Amra Delic | 2 | 26 | 5.07 |
| Gabriele Sottocornola | 3 | 3 | 3.08 |
| Jessie Smith | 4 | 0 | 0.34 |
| Nazareno Andrade | 5 | 615 | 43.57 |
| Luca Belli | 6 | 0 | 1.69 |
| Michael M. Bronstein | 7 | 4032 | 167.52 |
| Akshay Kumar Gupta | 8 | 7 | 1.83 |
| Sofia Ira Ktena | 9 | 69 | 6.83 |
| Alexandre Lung-Yut-Fong | 10 | 0 | 1.01 |
| Frank Portman | 11 | 0 | 1.01 |
| Alykhan Tejani | 12 | 37 | 4.14 |
| Yuanpu Xie | 13 | 104 | 8.80 |
| Xiao Zhu | 14 | 0 | 0.34 |
| Wenzhe Shi | 15 | 792 | 39.85 |