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A combination of classification based methods for recommending tweets
A 3 month long RecSys 2020 challenge1 was organized by Twitter[1]. Various kinds of implicit feedback with varying levels of sparsity were considered. In subsequent sections, I describe my approach to address this task, which helped me land on the 4th ...
A Stacking Ensemble Model for Prediction of Multi-type Tweet Engagements
The RecSys Challenge 2020 is a competition with a task of predicting four types of user engagements on Twitter: Like, Reply, Retweet and Retweet with comment. In this paper, we describe Team Wantedly’s approach to this challenge, which won the third ...
Leveraging User Embeddings and Text to Improve CTR Predictions With Deep Recommender Systems
- Carlos Miguel Patiño,
- Camilo Velásquez,
- Juan Manuel Muñoz,
- Juan Manuel Gutiérrez,
- David Ricardo Valencia,
- Cristian Bartolome Aramburu
Predicting user engagement, often framed as a CTR prediction problem, is important to maximize user satisfaction in social networks. The 2020 Recsys Challenge was sponsored by Twitter and set the goal of predicting four types of user engagement using a ...
GPU Accelerated Feature Engineering and Training for Recommender Systems
- Benedikt Schifferer,
- Gilberto Titericz,
- Chris Deotte,
- Christof Henkel,
- Kazuki Onodera,
- Jiwei Liu,
- Bojan Tunguz,
- Even Oldridge,
- Gabriel De Souza Pereira Moreira,
- Ahmet Erdem
In this paper we present our 1st place solution of the RecSys Challenge 2020 which focused on the prediction of user behavior, specifically the interaction with content, on this year’s dataset from competition host Twitter. Our approach achieved the ...
Gradient Boosting and Language Model Ensemble for Tweet Recommendation
In this paper, we describe the approach used on the RecSys Challenge 20201 which focuses on a real-world task of tweet engagement prediction. A large database of ∼ 160M public tweets, obtained by subsampling within 2 weeks, is used with the goal to ...
Multi-Objective Blended Ensemble For Highly Imbalanced Sequence Aware Tweet Engagement Prediction
- Nicolò Felicioni,
- Andrea Donati,
- Luca Conterio,
- Luca Bartoccioni,
- Davide Yi Xian Hu,
- Cesare Bernardis,
- Maurizio Ferrari Dacrema
In this paper we provide a description of the methods we used as team BanaNeverAlone for the ACM RecSys Challenge 2020, organized by Twitter. The challenge addresses the problem of user engagement prediction: the goal is to predict the probability of a ...
Engaging with Tweets: The Missing Dataset On Social Media
Most social media websites make use of recommender systems to show the content of interest for their users and to keep them engaged with the platform. On Twitter users can share and engage with the content by tweets. The ACM RecSys challenge 2020 ...
Predicting Twitter Engagement With Deep Language Models
- Maksims Volkovs,
- Zhaoyue Cheng,
- Mathieu Ravaut,
- Hojin Yang,
- Kevin Shen,
- Jin Peng Zhou,
- Anson Wong,
- Saba Zuberi,
- Ivan Zhang,
- Nick Frosst,
- Helen Ngo,
- Carol Chen,
- Bharat Venkitesh,
- Stephen Gou,
- Aidan N. Gomez
Twitter has become one of the main information sharing platforms for millions of users world-wide. Numerous tweets are created daily, many with highly time sensitive content such as breaking news, new multimedia content or personal updates. ...
Why Are Deep Learning Models Not Consistently Winning Recommender Systems Competitions Yet?: A Position Paper
For the past few years most published research on recommendation algorithms has been based on deep learning (DL) methods. Following common research practices in our field, these works usually demonstrate that a new DL method is outperforming other ...
- Proceedings of the Recommender Systems Challenge 2020
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Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| RecSys Challenge '16 | 15 | 11 | 73% |
| Overall | 15 | 11 | 73% |