The DOI matches its Crossref record; 181 of the 181 references it cites were found in a registry or an index.
Evaluating Recommender Systems: Survey and Framework
Open accessAbstract
The comprehensive evaluation of the performance of a recommender system is a complex endeavor: many facets need to be considered in configuring an adequate and effective evaluation setting. Such facets include, for instance, defining the specific goals of the evaluation, choosing an evaluation method, underlying data, and suitable evaluation metrics. In this article, we consolidate and systematically organize this dispersed knowledge on recommender systems evaluation. We introduce the Framework for Evaluating Recommender systems (FEVR), which we derive from the discourse on recommender systems evaluation. In FEVR, we categorize the evaluation space of recommender systems evaluation. We postulate that the comprehensive evaluation of a recommender system frequently requires considering multiple facets and perspectives in the evaluation. The FEVR framework provides a structured foundation to adopt adequate evaluation configurations that encompass this required multi-facetedness and provides the basis to advance in the field. We outline and discuss the challenges of a comprehensive evaluation of recommender systems and provide an outlook on what we need to embrace and do to move forward as a research community.
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Cites
Matrix Factorization Techniques for Recommender Systems
Koren, Y., Bell, R., & Volinsky, C. (2009) · Computer · doi:10.1109/mc.2009.263 · Cited by 11,673
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A Design Science Research Methodology for Information Systems Research
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“At the heart of any evaluation activity is the comparison of the objectives (target performance) to the observed results (actual performance) [170].”
ResultAttitude-behavior relations: A theoretical analysis and review of empirical research
Ajzen, I., & Fishbein, M. (1977) · Psychological Bulletin · doi:10.1037/0033-2909.84.5.888 · Cited by 7,611 · cited 1×
“However, self-reports may not be consistent with user behavior [43, 141, 211] because the link between an individual’s attitude and behavior is generally not very strong [7].”
Neural Collaborative Filtering
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.-S. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052569 · Cited by 6,652
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
Cumulated gain-based evaluation of IR techniques
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LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
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The MovieLens Datasets: History and Context
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Cited by
Recommender Systems in the Era of Large Language Models (LLMs)
Zhao, Z., Fan, W., Li, J., Liu, Y., Mei, X., Wang, Y., Wen, Z., Wang, F., Zhao, X., Tang, J., et al. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3392335 · Cited by 481
Metadata
- Authors
- Zangerle, E., & Bauer, C.
- Venue
- ACM Computing Surveys
- Published
- 2022-12-23
- Volume
- 55
- Issue
- 8
- Pages
- 1-38
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W4292092796
- Cited by
- 283
- References
- 181
- FWCI
- 51.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
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