When used effectively, recommender systems provide users with suggestions based on their own preferences. These systems first showed their value with e-commerce sites like Amazon and eBay, which provided recommendations algorithmically. A key drawback of these systems is that some items need personal touch recommendations to spur on purchase, use, or consumption. A recommender system that facilitates personal touch recommendations by enabling users to discover good recommenders as opposed to focusing on recommending items algorithmically addresses this drawback. In this article, we discuss such a system-a curated recommender system. A curated recommender system is optimal for online retailers and service providers, especially those that sell books, stream content, or provide social networking platforms.

【書誌情報】

ページ数：8ページ

サイズ：A4

商品番号：HBSP-BH838

発行日：2017/9/15

登録日：2017/12/7