Impression-Aware Recommender Systems
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Novel data sources bring new opportunities to improve the quality of recommender systems. Impressions are a novel data source containing past recommendations (shown items) and traditional interactions. Researchers may use impressions to refine user preferences and overcome the current limitations in recommender systems research. The relevance and interest of impressions have increased over the years; hence, the need for a review of relevant work on this type of recommenders. We present a systematic literature review on recommender systems using impressions, focusing on three fundamental angles in research: recommenders, datasets, and evaluation methodologies. We provide three categorizations of papers describing recommenders using impressions, present each reviewed paper in detail, describe datasets with impressions, and analyze the existing evaluation methodologies. Lastly, we present open questions and future directions of interest, highlighting aspects missing in the literature that can be addressed in future works.
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