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.

翻译：新型数据源为提高推荐系统质量带来了新的机遇，并作为个性化推荐新范式的催化剂。印象是一种新型数据源，包含用户屏幕上展示的条目。过去的研究主要利用交互行为提供个性化推荐，偶尔在可获得印象数据时加以利用。由于印象数据具备提供更精准推荐的潜力，对其关注度日益增加。尽管关注度提升，利用印象的推荐系统研究仍处于分散状态。许多研究对印象存在不同解读，并以多种不同方式将印象应用于推荐系统。为将这些解读统一至单一框架，本文对利用印象的推荐系统进行系统性文献综述，聚焦三个基本视角：推荐模型、数据集与评估方法。我们定义了理论框架以界定利用印象的推荐系统，并提出称为印象感知推荐系统的新型个性化推荐范式。为此范式中的推荐系统提出分类体系，并以此对既往研究中的推荐模型、数据集及评估方法进行分类。最后，我们指出开放性问题与未来方向，着重强调现有文献中的缺失维度。