SM-RS: Single- and Multi-Objective Recommendations with Contextual Impressions and Beyond-Accuracy Propensity Scores

Recommender systems (RS) rely on interaction data between users and items to generate effective results. Historically, RS aimed to deliver the most consistent (i.e., accurate) items to the trained user profiles. However, the attention towards additional (beyond-accuracy) quality criteria has increased tremendously in recent years. Both the research and applied models are being optimized for diversity, novelty, or fairness, to name a few. Naturally, the proper functioning of such optimization methods depends on the knowledge of users' propensities towards interacting with recommendations having certain quality criteria. However, so far, no dataset that captures such propensities exists. To bridge this research gap, we present SM-RS (single-objective + multi-objective recommendations dataset) that links users' self-declared propensity toward relevance, novelty, and diversity criteria with impressions and corresponding item selections. After presenting the dataset's collection procedure and basic statistics, we propose three tasks that are rarely available to conduct using existing RS datasets: impressions-aware click prediction, users' propensity scores prediction, and construction of recommendations proportional to the users' propensity scores. For each task, we also provide detailed evaluation procedures and competitive baselines. The dataset is available at https://osf.io/hkzje/.