Competitions, Challenges, Shared Tasks & Living Labs

To test your skills in recommender-system development and research, competitions, shared tasks, and living labs are ideal. Notable initiatives include

Kaggle is the major platform for machine learning competitions, which includes many recommender-systems competitions.

The ACM RecSys Challenge is co-hosted every year alongside the ACM RecSys conference. It is sponsored by major industry partners who provide a large and realistic dataset as well as an attractive prize.

TREC is the probably most renowned initiative in the field of information retrieval that provides datasets and shared tasks. Recommendations are not very often part of TREC though.

The CLEF Initiative organizes yearly competitions with shared tasks and living labs. Competitions focus on information retrieval, recommender systems, and many other domains.

News Relating to Competitions, Challenges, etc.

How to win the SIGIR eCommerce Challenge with Transformers (Gabriel Moreira / NVIDIA)

The NVIDIA team performed well on the Session-based recommendation task of the SIGIR eCommerce Coveo Data Challenge 2021. NVIDIA achieved 1st place on the ‘Subsequent Items Prediction Leaderboard’ and 2nd on the ‘Next Item Prediction’ Leaderboard. These successes follow the previous successes of NVidia at the ACM RecSys Challenge and other recommender-system challenges. Gabriel Moreira […]

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NVIDIA Team wins the ACM RecSys Challenge 2020

It seems that NVIDIA becomes a new big player in the recommender-system community. After introducing Merlin – a Framework for Deep Recommender Systems – and offering an online course for Building Intelligent Recommender Systems, a team of NVIDIA employees now won the ACM RecSys Challenge 2020. The goal of the challenge was to “predict the […]

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The RecSys Challenge 2020 will be 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, […]

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