# ContentWise Impressions: An Industrial Dataset with Impressions Included

arXiv: https://arxiv.org/abs/2008.01212 · HTML: https://mlsanity.com/paper/2008.01212/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 8 of 8)

- [Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation](https://mlsanity.com/paper/2401.07769/) (2024)
- [Impression-Aware Recommender Systems](https://mlsanity.com/paper/2308.07857/) (2023)
- [Neural Click Models for Recommender Systems](https://mlsanity.com/paper/2409.20055/) (2024)
- [A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels](https://mlsanity.com/paper/2105.06275/) (2021)
- [Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels](https://mlsanity.com/paper/2105.07062/) (2021)
- [Modeling Social Media Recommendation Impacts Using Academic Networks: A Graph Neural Network Approach](https://mlsanity.com/paper/2410.04552/) (2024)
- [On the Reliability of Sampling Strategies in Offline Recommender Evaluation](https://mlsanity.com/paper/2508.05398/) (2025)
- [FARE: Deep Reinforcement Learning For Fair Exposure Constrained Uncertainty Aware Financial Content Personalization](https://mlsanity.com/paper/2609.31890/) (2026)

## Abstract (arXiv)

In this article, we introduce the ContentWise Impressions dataset, a collection of implicit interactions and impressions of movies and TV series from an Over-The-Top media service, which delivers its media contents over the Internet. The dataset is distinguished from other already available multimedia recommendation datasets by the availability of impressions, i.e., the recommendations shown to the user, its size, and by being open-source. We describe the data collection process, the preprocessing applied, its characteristics, and statistics when compared to other commonly used datasets. We also highlight several possible use cases and research questions that can benefit from the availability of user impressions in an open-source dataset. Furthermore, we release software tools to load and split the data, as well as examples of how to use both user interactions and impressions in several common recommendation algorithms.