Impressions in Recommender Systems: Present and Future

Impressions are a novel data source providing researchers and practitioners with more details about user interactions and their context. In particular, an impression contain the items shown on screen to users, alongside users' interactions toward such items. In recent years, interest in impressions has thrived, and more papers use impressions in recommender systems. Despite this, the literature does not contain a comprehensive review of the current topics and future directions. This work summarizes impressions in recommender systems under three perspectives: recommendation models, datasets with impressions, and evaluation methodologies. Then, we propose several future directions with an emphasis on novel approaches. This work is part of an ongoing review of impressions in recommender systems

Characterizing Impression-Aware Recommender Systems

Impression-aware recommender systems (IARS) are a type of recommenders that learn user preferences using their interactions and the recommendations (also known as impressions) shown to users. The community’s interest in this type of recommenders has steadily increased in recent years. To aid in characterizing this type of recommenders, we propose a theoretical framework to define IARS and classify the recommenders present in the state-of-the-art. We start this work by defining core concepts related to this type of recommenders, such as impressions and user feedback. Based on this theoretical framework, we identify and define three properties and three taxonomies that characterize IARS. Lastly, we undergo a systematic literature review where we discover and select papers belonging to the state-of-the-art. Our review analyzes papers under the properties and taxonomies we propose; we highlight the most and least common properties and taxonomies used in the literature, their relations, and their evolution over time, among others

A Survey on Automated Fact-Checking

Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem. Therefore, researchers have been exploring how factchecking can be automated, using techniques based on natural language processing, machine learning, knowledge representation, and databases to automatically predict the veracity of claims. In this paper, we survey automated fact-checking stemming from natural language processing, and discuss its connections to related tasks and disciplines. In this process, we present an overview of existing datasets and models, aiming to unify the various definitions given and identify common concepts. Finally, we highlight challenges for future research

Impression-Aware Recommender Systems

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.Comment: 34 pages, 103 references, 6 tables, 2 figures, ACM UNDER REVIE