The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
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(2012) · Encyclopedia of the Sciences of Learning · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,370
Baeza-Yates, R. A., & Ribeiro-Neto, B. A. (1999) · Cited by 11,505
Adomavicius, G., & Tuzhilin, A. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.99 · Cited by 10,310
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
Understanding and Using Context
Dey, A. K. (2001) · Personal and Ubiquitous Computing · doi:10.1007/s007790170019 · Cited by 5,012
A Multilinear Singular Value Decomposition
De Lathauwer, L., De Moor, B., & Vandewalle, J. (2000) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/s0895479896305696 · Cited by 4,106
Hybrid Recommender Systems: Survey and Experiments
Burke, R. (2002) · User Modeling and User-Adapted Interaction · doi:10.1023/a:1021240730564 · Cited by 3,871
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
Dietterich, T. G. (1998) · Neural Computation · doi:10.1162/089976698300017197 · Cited by 3,840
(2011) · Springer eBooks · doi:10.1007/978-0-387-85820-3 · Cited by 3,761
A survey of cross-validation procedures for model selectionPreprint
Arlot, S., & Célisse, A. (2010) · Cited by 3,625
Cited by
Sequential User-based Recurrent Neural Network Recommendations
Donkers, T., Loepp, B., & Ziegler, J. (2017) · Proceedings of the Eleventh ACM Conference on Recommender Systems · doi:10.1145/3109859.3109877 · Cited by 359
News recommender systems – Survey and roads ahead
Karimi, M., Jannach, D., & Jugovac, M. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.04.008 · Cited by 336
Multistakeholder recommendation: Survey and research directions
Abdollahpouri, H., Adomavicius, G., Burke, R., Guy, I., Jannach, D., Kamishima, T., Krasnodebski, J., & Pizzato, L. (2020) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-019-09256-1 · Cited by 319
Latent Cross: Making Use of Context in Recurrent Recommender Systems
Beutel, A., Covington, P., Jain, S., Xu, C., Li, J., Gatto, V., & Chi, E. H. (2018) · Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining · doi:10.1145/3159652.3159727 · Cited by 313
Metadata
- Authors
- Campos, P. G., Díez, F., & Cantador, I.
- Venue
- User Modeling and User-Adapted Interaction
- Published
- 2013-02-15
- Volume
- 24
- Issue
- 1-2
- Pages
- 67-119
- ISSN
- 0924-1868 (ISSN-L)
- 1573-1391
- OpenAlex
- W2072992969
- Cited by
- 437
- References
- 81
- FWCI
- 77.98
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. https://app.cochat.ai/papers/10.1007/s11257-012-9136-x
- The paper
- Campos, P. G., Díez, F., & Cantador, I. (2014). Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Modeling and User-Adapted Interaction. https://doi.org/10.1007/s11257-012-9136-x
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.
