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Social information filtering: algorithms for automating “word of mouth”
Open accessAbstract
This paper describes a technique for making personalized recommendations from any type of database to a user based on similarities between the interest profile of that user and those of other users.In particular, we discuss the implementation of a networked system called Ringo, which makes personalized recommendations for music albums and artists.Ringo's database of users and artists grows dynamically as more people use the system and enter more information.Four different algorithms for making recommendations by using social information filtering were tested and compared.We present quantitative and qualitative results obtained from the use of Ringo by more than 2000 people.
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Cited by
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Candan, K. S., Dönderler, M. E., Hedgpeth, T., Kim, J. W., Li, Q., & Sapino, M. L. (2009) · ACM Transactions on Information Systems · doi:10.1145/1508850.1508853 · Cited by 8
Metadata
- Authors
- Shardanand, U., & Maes, P.
- Venue
- Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '95
- Publisher
- ACM Press
- Published
- 1995
- Pages
- 210-217
- OpenAlex
- W2124591829
- Cited by
- 2,823
- References
- 0
- FWCI
- 49.13
- Type
- conference paper
- Language
- en
- Open access
- bronze
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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.
