Recommender Systems for Self-Actualization

被引：47

作者：

Knijnenburg, Bart P. ^{[1
]}

Sivakumar, Saadhika ^{[1
]}

Wilkinson, Daricia ^{[1
]}

机构：

[1] Clemson Univ, Sch Comp, 215 McAdams Hall, Clemson, SC 29634 USA

来源：

PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16) | 2016年

基金：

美国国家科学基金会;

关键词：

Recommender Systems; Filter Bubble; Choice Overload; Self-Actualization; CHOICE;

D O I：

10.1145/2959100.2959189

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：11 / 14

页数：4

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