Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video Recommendation-Reference-Cited by-同舟云学术

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Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video Recommendation

Published:2024-05-13 Issue: Volume:31 Page:4727-4735
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
language:
Short-container-title:

Author:

Sukiennik Nicholas¹^ORCID,Gao Chen²^ORCID,Li Nian³^ORCID

Affiliation:

1. Department of Electronic Engineering, Tsinghua University & Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China

2. Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China

3. Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

Funder

National Key Research and Development Program of China

Guoqiang Institute, Tsinghua University

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3648159

Reference37 articles.

1. Algorithmic Effects on the Diversity of Consumption on Spotify

2. Eytan Bakshy, Solomon Messing, and Lada A. Adamic. 2015. Exposure to ideologically diverse news and opinion on Facebook. Science, Vol. 348, 6239 (2015), 1130--1132.

3. The use of MMR, diversity-based reranking for reordering documents and producing summaries

4. Laming Chen, Guoxin Zhang, and Eric Zhou. 2018. Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity. In Advances in Neural Information Processing Systems, Vol. 31. Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2018/hash/dbbf603ff0e99629dda5d75b6f75f966-Abstract.html

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