ContentWise Impressions: An Industrial Dataset with Impressions Included

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.Comment: 8 pages, 2 figure

An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering

This work explores the reproducibility of CFGAN. CFGAN and its family of models (TagRec, MTPR, and CRGAN) learn to generate personalized and fake-but-realistic rankings of preferences for top-N recommendations by using previous interactions. This work successfully replicates the results published in the original paper and discusses the impact of certain differences between the CFGAN framework and the model used in the original evaluation. The absence of random noise and the use of real user profiles as condition vectors leaves the generator prone to learn a degenerate solution in which the output vector is identical to the input vector, therefore, behaving essentially as a simple autoencoder. The work further expands the experimental analysis comparing CFGAN against a selection of simple and well-known properly optimized baselines, observing that CFGAN is not consistently competitive against them despite its high computational cost. To ensure the reproducibility of these analyses, this work describes the experimental methodology and publishes all datasets and source code

Lightweight and Scalable Model for Tweet Engagements Predictions in a Resource-constrained Environment

In this paper we provide an overview of the approach we used as team Trial&Error for the ACM RecSys Challenge 2021. The competition, organized by Twitter, addresses the problem of predicting different categories of user engagements (Like, Reply, Retweet and Retweet with Comment), given a dataset of previous interactions on the Twitter platform. Our proposed method relies on efficiently leveraging the massive amount of data, crafting a wide variety of features and designing a lightweight solution. This results in a significant reduction of computational resources requirements, both during the training and inference phase. The final model, an optimized LightGBM, allowed our team to reach the 4th position in the final leaderboard and to rank 1st among the academic teams