Introduction to Recommender Systems Handbook
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The Pearson correlation coefficient takes into account the mean and standard deviation of ratings, making it more robust to variations in rating magnitude.
“Unlike Euclidean distance, the Pearson correlation coefficient takes into account the mean and standard deviation of the ratings, which makes it more robust to variations in the magnitude of ratings (Ricci et al. 2011).”
From AdaptUI: A Framework for the development of Adaptive User Interfaces in Smart Product-Service Systems (2024) · cites this paper as Ricci et al. 2011
Euclidean distance is outperformed as k increases possibly because it does not take into account correlations among users beyond pairwise distance.
“However, as k increases, Euclidean distance is outperformed by cosine and Pearson, possibly because does not take into account the correlations among users beyond their pairwise distance (Ricci et al. 2011).”
From AdaptUI: A Framework for the development of Adaptive User Interfaces in Smart Product-Service Systems (2024) · cites this paper as Ricci et al. 2011
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Fink, O., Wang, Q., Svensén, M., Dersin, P., Lee, W.-J., & Ducoffe, M. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103678 · Cited by 549 · see citing works
Self-supervised learning for recommender systems: A survey
Yu, J., Yin, H., Xia, X., Chen, T., Li, J., & Huang, Z. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3282907 · Cited by 413 · see citing works
Möller, J., Trilling, D., Helberger, N., & van Es, B. (2018) · Information, Communication & Society · doi:10.1080/1369118x.2018.1444076 · Cited by 387 · see citing works
Neural Rating Regression with Abstractive Tips Generation for Recommendation
Li, P., Wang, Z., Ren, Z., Bing, L., & Lam, W. (2017) · Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3077136.3080822 · Cited by 313 · see citing works
A Survey on Bayesian Deep Learning
Wang, H., & Yeung, D.-Y. (2021) · ACM Computing Surveys · doi:10.1145/3409383 · Cited by 247 · see citing works
Futures of artificial intelligence through technology readiness levels
Martínez-Plumed, F., Gómez, E., & Hernández-Orallo, J. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2020.101525 · Cited by 130 · see citing works
Break the Loop: Gender Imbalance in Music Recommenders
Ferraro, A., Serra, X., & Bauer, C. (2021) · Proceedings of the 2021 Conference on Human Information Interaction and Retrieval · doi:10.1145/3406522.3446033 · Cited by 92 · see citing works
Factors influencing willingness to provide personal information for personalized recommendations
Kim, M. S., & Kim, S. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.06.031 · Cited by 78 · see citing works
Carrasco-Hernandez, L., Jódar-Sánchez, F., Núñez-Benjumea, F., Moreno Conde, J., Mesa González, M., Civit-Balcells, A., Hors-Fraile, S., Parra-Calderón, C. L., Bamidis, P. D., & Ortega-Ruiz, F. (2020) · JMIR mHealth and uHealth · doi:10.2196/17530 · Cited by 65 · see citing works
Fairness metrics and bias mitigation strategies for rating predictions
Ashokan, A., & Haas, C. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102646 · Cited by 47 · see citing works
An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model
Guo, Y., Wang, M., & Li, X. (2017) · Symmetry · doi:10.3390/sym9100216 · Cited by 36 · see citing works
New SVD-based collaborative filtering algorithms with differential privacy
Xian, Z., Li, Q., Huang, X., & Li, L. (2017) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-162053 · Cited by 35 · see citing works
Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce
Paraschakis, D. (2018) · doi:10.24834/2043/24268 · Cited by 35 · see citing works
Carrera-Rivera, A., Larrinaga, F., Lasa, G., Martinez-Arellano, G., & Unamuno, G. (2024) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-024-09414-0 · Cited by 30 · see citing works
Applying Bayesian Network to Assess the Levels of Skills Mastering in Adaptive Dynamic OER-Systems
Nekhaev, I., Zhuykov, I., Manukyants, S., & Maslennikov, A. (2020) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-030-63322-6_94 · Cited by 3 · see citing works
- Authors
- Ricci, F., Rokach, L., & Shapira, B.
- Affiliations
- Ben-Gurion University of the Negev (IL)
- Venue
- Recommender Systems Handbook
- Publisher
- Springer US
- Published
- 2010-10-05
- Pages
- 1-35
- OpenAlex
- W1486317198
- Cited by
- 2,378
- References
- 1
- Citations by year
- 2026: 59 · 2025: 104 · 2024: 144 · 2023: 149 · 2022: 191 · 2021: 239 · 2020: 214 · 2019: 176 · 2018: 234 · 2017: 252 · 2016: 219 · 2015: 167 · 2014: 107 · 2013: 80 · 2012: 34
- FWCI
- 111.05
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- Locations
- Springer eBooks (publishedVersion)
- Open Research (University of Surrey) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- recommender systems, product recommendation, user preferences, recommendation techniques
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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