research-article

News recommender system: a review of recent progress, challenges, and opportunities

Authors:

Chen DingAuthors Info & Claims

Artificial Intelligence Review, Volume 55, Issue 1

Pages 749 - 800

https://doi.org/10.1007/s10462-021-10043-x

Published: 01 January 2022 Publication History

Abstract

Nowadays, more and more news readers read news online where they have access to millions of news articles from multiple sources. In order to help users find the right and relevant content, news recommender systems (NRS) are developed to relieve the information overload problem and suggest news items that might be of interest for the news readers. In this paper, we highlight the major challenges faced by the NRS and identify the possible solutions from the state-of-the-art. Our discussion is divided into two parts. In the first part, we present an overview of the recommendation solutions, datasets, evaluation criteria beyond accuracy and recommendation platforms being used in the NRS. We also talk about two popular classes of models that have been successfully used in recent years. In the second part, we focus on the deep neural networks as solutions to build the NRS. Different from previous surveys, we study the effects of news recommendations on user behaviors and try to suggest possible remedies to mitigate those effects. By providing the state-of-the-art knowledge, this survey can help researchers and professional practitioners have a better understanding of the recent developments in news recommendation algorithms. In addition, this survey sheds light on the potential new directions.

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Cited By

Kruse JLindskow KKalloori SPolignano MPomo CSrivastava AUppal AAndersen MFrellsen J(2024)EB-NeRD a large-scale dataset for news recommendationProceedings of the Recommender Systems Challenge 202410.1145/3687151.3687152(1-11)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3687151.3687152
Xu LLiu YXu TChen ETang Y(2024)Graph Augmentation Empowered Contrastive Learning for RecommendationACM Transactions on Information Systems10.1145/367737743:2(1-27)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3677377
Möller LPadó S(2024)Explaining Neural News Recommendation with Attributions onto Reading HistoriesACM Transactions on Intelligent Systems and Technology10.1145/367323316:1(1-25)Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3673233
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Index Terms

News recommender system: a review of recent progress, challenges, and opportunities
1. Computing methodologies
  1. Machine learning
2. Information systems

Index terms have been assigned to the content through auto-classification.

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cover image Artificial Intelligence Review

Artificial Intelligence Review Volume 55, Issue 1

Jan 2022

793 pages

ISSN:0269-2821

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© Crown 2021.

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Kluwer Academic Publishers

United States

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Published: 01 January 2022

Accepted: 09 July 2021

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Kruse JLindskow KKalloori SPolignano MPomo CSrivastava AUppal AAndersen MFrellsen J(2024)EB-NeRD a large-scale dataset for news recommendationProceedings of the Recommender Systems Challenge 202410.1145/3687151.3687152(1-11)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3687151.3687152
Xu LLiu YXu TChen ETang Y(2024)Graph Augmentation Empowered Contrastive Learning for RecommendationACM Transactions on Information Systems10.1145/367737743:2(1-27)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3677377
Möller LPadó S(2024)Explaining Neural News Recommendation with Attributions onto Reading HistoriesACM Transactions on Intelligent Systems and Technology10.1145/367323316:1(1-25)Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3673233
Bauer CBagchi CHundogan Ovan Es K(2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3654805
Wang SWang WZhang XWang YLiu HChen FBaeza-Yates RBonchi F(2024)A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671944(3200-3211)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671944
He PShi JMa WZheng X(2024)Broad collaborative filtering with adjusted cosine similarity by fusing matrix completionApplied Soft Computing10.1016/j.asoc.2024.112075165:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.112075
Pathak RSpezzano F(2024)An Empirical Analysis of Intervention Strategies’ Effectiveness for Countering Misinformation Amplification by Recommendation AlgorithmsAdvances in Information Retrieval10.1007/978-3-031-56066-8_23(285-301)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56066-8_23
Wang ZFu X(2023)Enhanced Semantic Matching with Topic-aware and Fine-grained User Modeling for News RecommendationProceedings of the 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology10.1145/3625403.3625437(189-195)Online publication date: 15-Sep-2023
https://dl.acm.org/doi/10.1145/3625403.3625437
Michiels LVannieuwenhuyze JLeysen JVerachtert RSmets AGoethals B(2023)How Should We Measure Filter Bubbles? A Regression Model and Evidence for Online NewsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608805(640-651)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608805
Khritankov A(2023)Positive feedback loops lead to concept drift in machine learning systemsApplied Intelligence10.1007/s10489-023-04615-353:19(22648-22666)Online publication date: 30-Jun-2023
https://dl.acm.org/doi/10.1007/s10489-023-04615-3
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