Fab: content-based, collaborative recommendation
Open accessAbstract
By combining both collaborative and content-based filtering systems, Fab may eliminate many of the weaknesses found in each approach.
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The input to recommender systems can be a search term or some other preference statement, and selection is traditionally based on the personal history of the user (content-based filtering), the cross-section of other users (collaborative filtering), or a mix of both.
“The input can be in the form of a search term or some other preference statement, and the selection is traditionally based on the personal history of the user (content-based filtering), or the cross-section of other users (collaborative filtering), or a mix of both (Balabanović & Shoham, 1997).”
From Do Search Algorithms Endanger Democracy? An Experimental Investigation of Algorithm Effects on Political Polarization (2020) · cites this paper as Balabanović & Shoham, 1997
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Discovering user communities on the Internet using unsupervised machine learning techniques
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- Authors
- Balabanović, M., & Shoham, Y.
- Affiliations
- Stanford University (US)
- ORCID
- Balabanović, Marko
- Venue
- Communications of the ACM
- Published
- 1997-03
- Volume
- 40
- Issue
- 3
- Pages
- 66-72
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2043403353
- Cited by
- 2,934
- References
- 0
- Citations by year
- 2026: 15 · 2025: 24 · 2024: 63 · 2023: 47 · 2022: 70 · 2021: 86 · 2020: 100 · 2019: 93 · 2018: 119 · 2017: 119 · 2016: 120 · 2015: 154 · 2014: 153 · 2013: 181 · 2012: 189
- FWCI
- 100.95
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- Funders
- National Science Foundation (9411306, 9220645)
- National Aeronautics and Space Administration
- Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Data Mining
- Keywords
- collaborative filtering, content-based filtering, hybrid recommendation, recommender systems
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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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