The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
Abstract
Collaborative filtering is one of the most widely adopted and successful recommendation approaches. Unlike approaches based on intrinsic consumer and product characteristics, CF characterizes consumers and products implicitly by their previous interactions. The simplest example is to recommend the most popular products to all consumers. Researchers are advancing CF technologies in such areas as algorithm design, human- computer interaction design, consumer incentive analysis, and privacy protection.
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Citations
Cites
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Metadata
- Authors
- Huang, Z., Zeng, D., & Chen, H.
- Venue
- IEEE Intelligent Systems
- Published
- 2007-09
- Volume
- 22
- Issue
- 5
- Pages
- 68-78
- ISSN
- 1541-1672 (ISSN-L)
- OpenAlex
- W2596629674
- Cited by
- 299
- References
- 1
- FWCI
- 35.49
- Type
- journal article
- Language
- en
- Open access
- closed
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- Huang, Z., Zeng, D., & Chen, H. (2007). A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce. IEEE Intelligent Systems. https://doi.org/10.1109/mis.2007.4338497
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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.
