Collected research
Fake Co-visitation Injection Attacks to Recommender Systems
Recommenders that infer relatedness from users viewing two items together can be steered by injecting fabricated co-visitation records. Framing the choice of target items and injection counts as a constrained optimisation problem lets an attacker with a limited budget force chosen recommendations on services including YouTube, eBay, Amazon, Yelp and LinkedIn.
Record
- Researcher
- Guolei Yang, Neil Zhenqiang Gong and Ying Cai
- Published by
- NDSS Symposium
- Topic
- Injection
In the archive
Related sources
- NDSS 2017: Fake Co-visitation Injection Attacks to Recommender Systems
- NDSS 2017: Fake Co-visitation Injection Attacks to Recommender Systems
Tags
This page is the archive's own catalogue record. The research is the work of Guolei Yang, Neil Zhenqiang Gong and Ying Cai, first published at the original source. Preserved copies are kept so the citation survives its host; this one was last captured on .