Impression-Aware Recommender Systems

TL;DR

This paper systematically reviews impression-aware recommender systems, unifying diverse interpretations of impression data, and introduces a theoretical framework and classification system for this emerging paradigm.

Contribution

It provides a comprehensive review, a unified framework, and a classification system for impression-aware recommender systems, highlighting future research directions.

Findings

Unified interpretation of impressions in recommender systems

Classification system for impression-aware recommenders

Identification of open research questions and gaps

Abstract

Novel data sources bring new opportunities to improve the quality of recommender systems and serve as a catalyst for the creation of new paradigms on personalized recommendations. Impressions are a novel data source containing the items shown to users on their screens. Past research focused on providing personalized recommendations using interactions, and occasionally using impressions when such a data source was available. Interest in impressions has increased due to their potential to provide more accurate recommendations. Despite this increased interest, research in recommender systems using impressions is still dispersed. Many works have distinct interpretations of impressions and use impressions in recommender systems in numerous different manners. To unify those interpretations into a single framework, we present a systematic literature review on recommender systems using impressions, focusing on three fundamental perspectives: recommendation models, datasets, and evaluation methodologies. We define a theoretical framework to delimit recommender systems using impressions and a novel paradigm for personalized recommendations, called impression-aware recommender systems. We propose a classification system for recommenders in this paradigm, which we use to categorize the recommendation models, datasets, and evaluation methodologies used in past research. Lastly, we identify open questions and future directions, highlighting missing aspects in the reviewed literature.