Recommender Systems for Self-Actualization

Bart P. Knijnenburg, S. Sivakumar, Daricia Wilkinson
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引用次数: 70

Abstract

Every day, we are confronted with an abundance of decisions that require us to choose from a seemingly endless number of choice options. Recommender systems are supposed to help us deal with this formidable task, but some scholars claim that these systems instead put us inside a "Filter Bubble" that severely limits our perspectives. This paper presents a new direction for recommender systems research with the main goal of supporting users in developing, exploring, and understanding their unique personal preferences.
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自我实现的推荐系统
每天,我们都面临着大量的决定,这些决定要求我们从看似无穷无尽的选择中做出选择。推荐系统本应帮助我们处理这一艰巨的任务,但一些学者声称,这些系统反而将我们置于一个“过滤气泡”中,严重限制了我们的视角。本文提出了推荐系统研究的新方向,其主要目标是支持用户开发、探索和理解他们独特的个人偏好。
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