The DOI matches its Crossref record.
E-Commerce Recommendation Applications
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Amazon.com recommendations: item-to-item collaborative filtering
Linden, G., Smith, B., & York, J. (2003) · IEEE Internet Computing · doi:10.1109/mic.2003.1167344 · Cited by 5,403
The business model ontology a proposition in a design science approach
Osterwalder, A. (2004) · Unknown · doi:10.22005/bcu.15985 · Cited by 1,932
E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact1
Xiao, B., & Benbasat, I. (2007) · MIS Quarterly · doi:10.2307/25148784 · Cited by 1,081
How Can Recommender Systems Benefit from Large Language Models: A Survey
Lin, J., Dai, X., Xi, Y., Liu, W., Chen, B., Zhang, H., Liu, Y., Wu, C., Li, X., Zhu, C., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3678004 · Cited by 242
Recommender Systems: Techniques, Applications, and Challenges
Ricci, F., Rokach, L., & Shapira, B. (2022) · Recommender Systems Handbook · doi:10.1007/978-1-0716-2197-4_1 · Cited by 214
Degenerate Feedback Loops in Recommender Systems
Jiang, R., Chiappa, S., Lattimore, T., György, A., & Kohli, P. (2019) · Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3306618.3314288 · Cited by 232
A tourism recommender system based on collaboration and text analysis
LOH, S., LORENZI, F., SALDAÑA, R., & LICTHNOW, D. (2003) · Information Technology & Tourism · doi:10.3727/1098305031436980 · Cited by 111
User Attitudes Regarding a User-Adaptive eCommerce Web Site
Alpert, S. R., Karat, J., Karat, C.-M., Brodie, C., & Vergo, J. G. (2003) · User Modeling and User-Adapted Interaction · doi:10.1023/a:1026201108015 · Cited by 99
The leniency of return policy and consumers' repurchase intention in online retailing
Wang, Y., Anderson, J., Joo, S.-J., & Huscroft, J. R. (2019) · Industrial Management & Data Systems · doi:10.1108/imds-01-2019-0016 · Cited by 80
Understanding Human-Machine Networks: A Cross-Disciplinary Survey
Tsvetkova, M., Yasseri, T., Meyer, E. T., Pickering, J. B., Engen, V., Walland, P., Lüders, M., Følstad, A., & Bravos, G. (2018) · ACM Computing Surveys · doi:10.1145/3039868 · Cited by 67
FCMF: Federated collective matrix factorization for heterogeneous collaborative filtering
Yang, E., Huang, Y., Liang, F., Pan, W., & Ming, Z. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106946 · Cited by 58
Wang, Y., Gao, M., Ran, X., Ma, J., & Zhang, L. Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119457 · Cited by 42
(2012) · Pacific Asia Conference on Information Systems · Cited by 34
Personalization and probabilities: Impersonal propensities in online grocery shopping
Mackenzie, A. (2018) · Big Data & Society · doi:10.1177/2053951718778310 · Cited by 31
(2006) · Open Research (University of Surrey) · Cited by 23
Metadata
- Authors
- Schafer, J. B., Konstan, J. A., & Riedl, J.
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2001-01
- Volume
- 5
- Issue
- 1-2
- Pages
- 115-153
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W1570159982
- Cited by
- 1,530
- References
- 0
- FWCI
- 158.01
- Type
- journal article
- Language
- en
- Open access
- closed
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.