News recommender systems - Survey and roads ahead

By Mozhgan Karimi, Dietmar Jannach, Michael Jugovac. Information Processing & Management, 2018

326 citations recorded by the source corpus.

Abstract More and more people read the news online, e.g., by visiting the websites of their favorite newspapers or by navigating the sites of news aggregators. However, the abundance of news information that is published online every day through different channels can make it challenging for readers to locate the content they are interested in. The goal of News Recommender Systems (NRS) is to make reading suggestions to users in a personalized way. Due to their practical relevance, a variety of technical approaches to build such systems have been proposed over the last two decades. In this work, we review the state-of-the-art of designing and evaluating news recommender systems over the last ten years. One main goal of the work is to analyze which particular challenges of news recommendation (e.g., short item life times and recency aspects) have been well explored and which areas still require more work. Furthermore, in contrast to previous surveys, the paper specifically discusses methodological questions and today’s academic practice of evaluating and comparing different algorithmic news recommendation approaches based on accuracy measures.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/0c74cfc2a0a5cdc75d484b97c3d266ce6cfa65aa

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In Artificial Intelligence

In Information Systems

In Management Science and Operations Research

Citations in the corpus

Recommender systems and their ethical challenges (2020, background): “Inevitably, they end up shaping individual experience of digital environments and social interactions (Burr et al. 2018; de Vries 2010; Karimi et al. 2018).”
Reinforcement Learning based Recommender Systems: A Survey (2021, background): “Today, RSs are an essential part of most giant companies, like Google, Facebook, Amazon, and Netflix, and employed in a wide range of applications, including entertainment [4–6], e-commerce [7], news [8], e-learning [9], and healthcare [10].”
Evaluation of session-based recommendation algorithms (2018, background): “We in particular consider the news domain because it has certain distinct characteristics [Karimi et al. 2018].”