Workshop on Learning and Evaluating Recommendations with Impressions (LERI)

Abstract

This volume contains the papers presented at the Workshop on Learning and Evaluating Recommendations with Impressions (LERI), held in conjunction with the 17th ACM Conference on Recommender Systems (RecSys 2023). Recommender systems typically rely on past user interactions as the primary source of information for making predictions. However, although highly informative, past user interactions are strongly biased. Impressions, on the other hand, are a new source of information that indicate the items displayed on screen when the user interacted (or not) with them, and have the potential to impact the field of recommender systems in several ways. Early research on impressions was constrained by the limited availability of public datasets, but this is rapidly changing and, as a consequence, interest in impressions has increased. Impressions present new research questions and opportunities, but also bring new challenges. Several works propose to use impressions as part of recommender models in various ways and discuss their information content. Others explore their potential in off-policy-estimation and reinforcement learning. Overall, the interest of the community is growing, but efforts in this direction remain disconnected. Therefore, one of the aims of the LERI workshop is to bring the community togethe