2020/07/06 by Yingqiang Ge, Shuya Zhao, Honglu Zhou +4
Computer Science · Decision Sciences · Physics and Astronomy · #Advanced Bandit Algorithms Research #Complex Network Analysis Techniques #Echo (communications protocol) #Matching (statistics) #Phenomenon #Recommender Systems and Techniques #Recommender system #Social media #cs.IR #cs.SI
paper · pdf · doi:10.1145/3397271.3401431
arxiv created 2020/07/06 · arxiv updated 2020/07/07 · openalex created_date 2020/07/10 · openalex publication_date 2020/07/25 · openalex updated_date 2026/07/29
Personalized recommendation benefits users in accessing contents of interests effectively. Current research on recommender systems mostly focuses on matching users with proper items based on user interests. However, significant efforts are missing to understand how the recommendations influence user preferences and behaviors, e.g., if and how recommendations result in echo chambers. Extensive efforts have been made in examining the phenomenon in online media and social network systems. Meanwhile, there are growing concerns that recommender systems might lead to the self-reinforcing of user's interests due to narrowed exposure of items, which may be the potential cause of echo chamber. In this paper, we aim to analyze the echo chamber phenomenon in Alibaba Taobao --- one of the largest e-commerce platforms in the world.