The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Group recommendations with rank aggregation and collaborative filtering
Abstract
The majority of recommender systems are designed to make recommendations for individual users. However, in some circumstances the items to be selected are not intended for personal usage but for a group; e.g., a DVD could be watched by a group of friends. In order to generate effective recommendations for a group the system must satisfy, as much as possible, the individual preferences of the group's members.
Overview
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Citations
Cites
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278
Metadata
- Authors
- Baltrunas, L., Makcinskas, T., & Ricci, F.
- Venue
- Proceedings of the fourth ACM conference on Recommender systems
- Publisher
- ACM
- Published
- 2010-09-26
- Pages
- 119-126
- OpenAlex
- W2003605635
- Cited by
- 490
- References
- 1
- FWCI
- 10.57
- Type
- conference paper
- Language
- en
- Open access
- closed
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Group recommendations with rank aggregation and collaborative filtering. https://app.cochat.ai/papers/10.1145/1864708.1864733
- The paper
- Baltrunas, L., Makcinskas, T., & Ricci, F. (2010). Group recommendations with rank aggregation and collaborative filtering. Proceedings of the fourth ACM conference on Recommender systems. https://doi.org/10.1145/1864708.1864733
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.
