Multi-Objective Group Discovery on the Social Web
Abstract
We are interested in discovering user groups from collaborative rating datasets of the form $\langle i, u, s\rangle $ , where $i \in \mathcal{I}$ , $u \in \mathcal{U}$ , and s is the integer rating that user u has assigned to item i . Each user has a set of attributes that help find labeled groups such as young computer scientists in France and American female designers . We formalize the problem of finding user groups whose quality is optimized in multiple dimensions and show that it is NP-Complete. We develop $\alpha $ - MOMRI , an $\alpha $ -approximation algorithm, and h - MOMRI , a heuristic-based algorithm, for multi-objective optimization to find high quality groups. Our extensive experiments on real datasets from the social Web examine the performance of our algorithms and report cases where $\alpha $ - MOMRI and h - MOMRI are useful.
Cite
Text
Omidvar-Tehrani et al. "Multi-Objective Group Discovery on the Social Web." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46128-1_19Markdown
[Omidvar-Tehrani et al. "Multi-Objective Group Discovery on the Social Web." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/omidvartehrani2016ecmlpkdd-multiobjective/) doi:10.1007/978-3-319-46128-1_19BibTeX
@inproceedings{omidvartehrani2016ecmlpkdd-multiobjective,
title = {{Multi-Objective Group Discovery on the Social Web}},
author = {Omidvar-Tehrani, Behrooz and Amer-Yahia, Sihem and Dutot, Pierre-François and Trystram, Denis},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {296-312},
doi = {10.1007/978-3-319-46128-1_19},
url = {https://mlanthology.org/ecmlpkdd/2016/omidvartehrani2016ecmlpkdd-multiobjective/}
}