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Evaluating similarity measures: a large-scale study in the orkut social network
Abstract
Online information services have grown too large for users to navigate without the help of automated tools such as collaborative filtering, which makes recommendations to users based on their collective past behavior. While many similarity measures have been proposed and individually evaluated, they have not been evaluated relative to each other in a large real-world environment. We present an extensive empirical comparison of six distinct measures of similarity for recommending online communities to members of the Orkut social network. We determine the usefulness of the different recommendations by actually measuring users' propensity to visit and join recommended communities. We also examine how the ordering of recommendations influenced user selection, as well as interesting social issues that arise in recommending communities within a real social network.
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Cites
Item-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
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Social network sites: definition, history, and scholarship
boyd, D. M., & Ellison, N. B. (2007) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2007.00393.x · Cited by 16,251
Social structure of Facebook networks
Traud, A. L., Mucha, P. J., & Porter, M. A. (2012) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2011.12.021 · Cited by 707
Scaling up all pairs similarity search
Bayardo, R. J., Ma, Y., & Srikant, R. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242591 · Cited by 673
Efficient similarity joins for near duplicate detection
Xiao, C., Wang, W., Lin, X., & Yu, J. X. (2008) · Proceedings of the 17th international conference on World Wide Web · doi:10.1145/1367497.1367516 · Cited by 455
McFadden, J. (2021) · NeuroSci · doi:10.3390/neurosci2030021 · Cited by 13
Metadata
- Authors
- Spertus, E., Sahami, M., & Buyukkokten, O.
- Venue
- Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
- Publisher
- ACM
- Published
- 2005-08-21
- Pages
- 678-684
- OpenAlex
- W2114353347
- Cited by
- 296
- References
- 1
- FWCI
- 7.35
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Spertus, E., Sahami, M., & Buyukkokten, O. (2005). Evaluating similarity measures: a large-scale study in the orkut social network. Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. https://doi.org/10.1145/1081870.1081956
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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