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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2211.01179 |
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| _version_ | 1866929515198939136 |
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| author | Hoang, Lê Nguyên Beylerian, Romain Colbois, Bérangère Fageot, Julien Faucon, Louis Jungo, Aidan Noac'h, Alain Le Matissart, Adrien Villemaud, Oscar |
| author_facet | Hoang, Lê Nguyên Beylerian, Romain Colbois, Bérangère Fageot, Julien Faucon, Louis Jungo, Aidan Noac'h, Alain Le Matissart, Adrien Villemaud, Oscar |
| contents | This paper presents Solidago, an end-to-end modular pipeline to allow any community of users to collaboratively score any number of entities. Solidago proposes a six-module decomposition. First, it uses pretrust and peer-to-peer vouches to assign trust scores to users. Second, based on participation, trust scores are turned into voting rights per user per entity. Third, for each user, a preference model is learned from the user's evaluation data. Fourth, users' models are put on a similar scale. Fifth, these models are securely aggregated. Sixth, models are post-processed to yield human-readable global scores. We also propose default implementations of the six modules, including a novel trust propagation algorithm, and adaptations of state-of-the-art scaling and aggregation solutions. Our pipeline has been successfully deployed on the open-source platform tournesol.app. We thereby lay an appealing foundation for the collaborative, effective, scalable, fair, interpretable and secure scoring of any set of entities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_01179 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Solidago: A Modular Collaborative Scoring Pipeline Hoang, Lê Nguyên Beylerian, Romain Colbois, Bérangère Fageot, Julien Faucon, Louis Jungo, Aidan Noac'h, Alain Le Matissart, Adrien Villemaud, Oscar Social and Information Networks Cryptography and Security Computer Science and Game Theory This paper presents Solidago, an end-to-end modular pipeline to allow any community of users to collaboratively score any number of entities. Solidago proposes a six-module decomposition. First, it uses pretrust and peer-to-peer vouches to assign trust scores to users. Second, based on participation, trust scores are turned into voting rights per user per entity. Third, for each user, a preference model is learned from the user's evaluation data. Fourth, users' models are put on a similar scale. Fifth, these models are securely aggregated. Sixth, models are post-processed to yield human-readable global scores. We also propose default implementations of the six modules, including a novel trust propagation algorithm, and adaptations of state-of-the-art scaling and aggregation solutions. Our pipeline has been successfully deployed on the open-source platform tournesol.app. We thereby lay an appealing foundation for the collaborative, effective, scalable, fair, interpretable and secure scoring of any set of entities. |
| title | Solidago: A Modular Collaborative Scoring Pipeline |
| topic | Social and Information Networks Cryptography and Security Computer Science and Game Theory |
| url | https://arxiv.org/abs/2211.01179 |