Shilling Black-box Review-based Recommender Systems through Fake Review Generation
Author:
Affiliation:
1. National Tsing Hua University, HsinChu, Taiwan Roc
2. National Yang Ming Chiao Tung University, HsinChu, Taiwan Roc
Funder
National Science and Technology Council of Taiwan
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3580305.3599502
Reference61 articles.
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2. Arthur Brainskas Mirella Lapata and Ivan Titov. 2020. Few-Shot Learning for Opinion Summarization. In EMNLP. 4119--4135. Arthur Brainskas Mirella Lapata and Ivan Titov. 2020. Few-Shot Learning for Opinion Summarization. In EMNLP. 4119--4135.
3. Arthur Brainskas Mirella Lapata and Ivan Titov. 2021. Learning Opinion Summarizers by Selecting Informative Reviews. In EMNLP. 9424--9442. Arthur Brainskas Mirella Lapata and Ivan Titov. 2021. Learning Opinion Summarizers by Selecting Informative Reviews. In EMNLP. 9424--9442.
4. Robin Burke Bamshad Mobasher Chad Williams and Runa Bhaumik. 2006. Clas- sification Features for Attack Detection in Collaborative Recommender Systems. In KDD. 542--547. Robin Burke Bamshad Mobasher Chad Williams and Runa Bhaumik. 2006. Clas- sification Features for Attack Detection in Collaborative Recommender Systems. In KDD. 542--547.
5. Chong Chen Min Zhang Yiqun Liu and Shaoping Ma. 2018. Neural Attentional Rating Regression with Review-Level Explanations. In WWW. 1583--1592. Chong Chen Min Zhang Yiqun Liu and Shaoping Ma. 2018. Neural Attentional Rating Regression with Review-Level Explanations. In WWW. 1583--1592.
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