Recommendation Independence

Explore a conference talk on recommendation independence presented at FAT* 2018 by Toshihiro Kamishima and colleagues. Delve into the concept of fair treatment of content providers in recommendation systems. Learn about the regularization approach and model-based methods for achieving recommendation independence. Examine experimental results and comparisons with other approaches. Gain insights into the history and development of this field, as well as its implications for service pricing. Engage with the presented material through a question and answer session at the end of the talk.

Introduction
Overview
Cooperative Feeling
Recommendation Independence
Fair Treatment of Content Providers
Regularization Approach
History
Modelbased approach
Experimental results
Comparison
Service Price
Question