Paper 2024/106 Original: eprint.iacr.org/2024/106 ↗
A Trust-based Recommender System over Arbitrarily Partitioned Data with Privacy
Abstract
Recommender systems are effective mechanisms for recommendations about what to watch, read, or taste based on user ratings about experienced products or services. To achieve higher quality recommendations, e-commerce parties may prefer to collaborate over partitioned data. Due to privacy issues, they might hesitate to work in pairs and some solutions motivate them to collaborate. This study examines how to estimate trust-based predictions on arbitrarily partitioned data in which two parties have ratings for similar sets of customers and items. A privacy- preserving scheme is proposed, and it is justified that it efficiently offers trust-based predictions on partitioned data while preserving privacy.
Metadata
- Available format(s)
PDF- Category
- Applications
- Publication info
- Preprint.
- History
- 2024-01-24: last updated
- 2024-01-24: published
- Short URL
- https://ia.cr/2024/106
- License
- https://creativecommons.org/licenses/by/4.0/
BibTeX
@misc{cryptoeprint:2024/106,
author = {Ibrahim Yakut and Huseyin Polat},
title = {A Trust-based Recommender System over Arbitrarily Partitioned Data with Privacy},
howpublished = {Cryptology {ePrint} Archive, Paper 2024/106},
year = {2024},
url = {https://eprint.iacr.org/2024/106}
}