A Trust-based Recommender System over Arbitrarily Partitioned Data with Privacy

ePrint Report: A Trust-based Recommender System over Arbitrarily Partitioned Data with Privacy

Ibrahim Yakut, Huseyin Polat

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 …

commerce data e-commerce eprint report higher may privacy products quality recommendations recommender systems report services system systems trust watch