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By Murphy Stephen, G. Mark. 2014

Subfields: Recommender Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Recommender Systems

1. Neil Zhenqiang Gong
2. Deven R. Desai
3. M. Kapralov
4. Guolei Yang
5. Kunal Talwar
6. Rachel Cummings

Citations in the corpus

• Poisoning Attacks to Graph-Based Recommender Systems (2018, background): “[4] proposed privacy attacks to infer the items that a target user has rated before, e.”
• On differentially private low rank approximation (2013, background): “Similar privacy risks have been demonstrated in settings like Hunch and Amazon where no data is directly released (as in the Netflix challenge), but items similar to a given item are shown to the users [5].”
• The Role of Differential Privacy in GDPR Compliance (2018, methodology): “Earlier work [2] showed that collaborative filtering—the primary technique used in recommender systems— leaked information across users, where user i may learn personal”