Summary of Privacy-preserving Dynamic Assortment Selection, by Young Hyun Cho and Will Wei Sun
Privacy-Preserving Dynamic Assortment Selection
by Young Hyun Cho, Will Wei Sun
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework is presented for privacy-preserving dynamic assortment selection using multinomial logit (MNL) bandits. The approach employs a perturbed upper confidence bound method, integrating calibrated noise into user utility estimates to balance exploration and exploitation while ensuring robust privacy protection. The framework satisfies Joint Differential Privacy (JDP), which better suits dynamic environments than traditional differential privacy, effectively mitigating inference attack risks. Theoretical results include a near-optimal regret bound of () for the policy and explicit quantification of how privacy protection impacts regret. Simulations and an application to the Expedia hotel dataset demonstrate substantial performance enhancements over the benchmark method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to keep users’ information private when recommending personalized lists. This works by adding noise to user preferences while still making good recommendations. The method uses a type of bandit model called MNL, and it’s tested on real data from Expedia hotels. The results show that this approach can make better recommendations than others while keeping users’ data safe. |
Keywords
* Artificial intelligence * Inference
