The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A survey of serendipity in recommender systems
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Citations
Cites
Cumulated gain-based evaluation of IR techniques
Järvelin, K., & Kekäläinen, J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582418 · Cited by 4,841
Proceedings of the 2013 conference on Computer supported cooperative work
(2013) · doi:10.1145/2441776 · Cited by 601
Cited by
Möller, J., Trilling, D., Helberger, N., & van Es, B. (2018) · Information, Communication & Society · doi:10.1080/1369118x.2018.1444076 · Cited by 387
Hermann, E. (2022) · New Media & Society · doi:10.1177/14614448211022702 · Cited by 214
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
How Artificial Intelligence Constrains the Human Experience
Valenzuela, A., Puntoni, S., Hoffman, D., Castelo, N., De Freitas, J., Dietvorst, B., Hildebrand, C., Huh, Y. E., Meyer, R., Sweeney, M. E., et al. (2024) · Journal of the Association for Consumer Research · doi:10.1086/730709 · Cited by 81
Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives
Fu, Z., Niu, X., & Maher, M. L. (2024) · ACM Computing Surveys · doi:10.1145/3605145 · Cited by 30
User Bias in Beyond-Accuracy Measurement of Recommendation Algorithms
Wang, N., & Chen, L. (2021) · Fifteenth ACM Conference on Recommender Systems · doi:10.1145/3460231.3474244 · Cited by 22
Metadata
- Authors
- Kotkov, D., Wang, S., & Veijalainen, J.
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2016-11
- Volume
- 111
- Pages
- 180-192
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W2514091133
- Cited by
- 329
- References
- 2
- FWCI
- 66.88
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
