论文标题
迈向公平的对话推荐系统
Towards Fair Conversational Recommender Systems
论文作者
论文摘要
对话推荐系统已取得了巨大的成功。他们可以通过多轮交互周期准确地捕获用户当前的详细偏好，以有效地指导用户进行更个性化的建议。可惜，会话推荐系统可能会因偏见的不利影响而困扰，就像传统推荐人一样。在这项工作中，我们主张增加对这些新兴系统中偏差的存在和方法的关注。作为起点，我们提出了三个基本问题，应深入研究，以使会话推荐系统公平。
Conversational recommender systems have demonstrated great success. They can accurately capture a user's current detailed preference -- through a multi-round interaction cycle -- to effectively guide users to a more personalized recommendation. Alas, conversational recommender systems can be plagued by the adverse effects of bias, much like traditional recommenders. In this work, we argue for increased attention on the presence of and methods for counteracting bias in these emerging systems. As a starting point, we propose three fundamental questions that should be deeply examined to enable fairness in conversational recommender systems.
