The DOI matches its Crossref record; 101 of the 101 references it cites were found in a registry or an index.
News recommender systems – Survey and roads ahead
Abstract
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.
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Cites
Evaluating collaborative filtering recommender systems
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004) · ACM Transactions on Information Systems · doi:10.1145/963770.963772 · Cited by 5,798
A contextual-bandit approach to personalized news article recommendation
Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772758 · Cited by 2,562
Improving recommendation lists through topic diversification
Ziegler, C.-N., McNee, S. M., Konstan, J. A., & Lausen, G. (2005) · Proceedings of the 14th international conference on World Wide Web - WWW '05 · doi:10.1145/1060745.1060754 · Cited by 1,892
Google news personalization: scalable online collaborative filtering
Das, A. S., Datar, M., Garg, A., & Rajaram, S. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242610 · Cited by 1,511
The Netflix Recommender System: Algorithms, Business Value, and Innovation
Gomez-Uribe, C. A., & Hunt, N. (2016) · ACM Transactions on Management Information Systems · doi:10.1145/2843948 · Cited by 1,418
Performance of recommender algorithms on top-n recommendation tasks
Cremonesi, P., Koren, Y., & Turrin, R. (2010) · Proceedings of the fourth ACM conference on Recommender systems · doi:10.1145/1864708.1864721 · Cited by 1,410
Evaluating Recommendation Systems
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Recommender Systems: An Introduction
Jannach, D., Zanker, M., Felfernig, A., & FRIEDRICH, G. E. (2010) · Cited by 1,097
Self-citationA systematic review of systematic review process research in software engineering
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Cited by
Recommender systems and their ethical challenges
Milano, S., Taddeo, M., & Floridi, L. (2020) · AI & SOCIETY · doi:10.1007/s00146-020-00950-y · Cited by 556
Evaluation of session-based recommendation algorithms
Ludewig, M., & Jannach, D. (2018) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-018-9209-6 · Cited by 336
Self-citationPersonalized News Recommendation: Methods and Challenges
Wu, C., Wu, F., Huang, Y., & Xie, X. (2023) · ACM Transactions on Information Systems · doi:10.1145/3530257 · Cited by 240
Bias in Large Language Models: Origin, Evaluation, and Mitigation
Guo, Y., Guo, M., Su, J., Yang, Z., Zhu, M., Li, H., Qiu, M., & Liu, S. S. (2024) · arXiv · doi:10.48550/arxiv.2411.10915 · Cited by 208
Serendipity in Recommender Systems: A Systematic Literature Review
Ziarani, R. J., & Ravanmehr, R. (2021) · Journal of Computer Science and Technology · doi:10.1007/s11390-020-0135-9 · Cited by 123
Metadata
- Authors
- Karimi, M., Jannach, D., & Jugovac, M.
- Venue
- Information Processing & Management
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 54
- Issue
- 6
- Pages
- 1203-1227
- ISSN
- 0020-0271 (ISSN-L)
- 0306-4573
- OpenAlex
- W2805720864
- Cited by
- 336
- References
- 101
- FWCI
- 53.03
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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