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20202025
most citedRethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?

4 citations · 22 across the 19 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.LG2023

Communication-Efficient Federated Non-Linear Bandit Optimization

Chuanhao Li, Chong Liu, Yu-Xiang Wang

Federated optimization studies the problem of collaborative function optimization among multiple clients (e.g. mobile devices or organizations) under the coordination of a central…

cs.LG2023

Incentivized Communication for Federated Bandits

Zhepei Wei, Chuanhao Li, Haifeng Xu +1

Most existing works on federated bandits take it for granted that all clients are altruistic about sharing their data with the server for the collective good whenever needed. Despi…

cs.LG2023

Pure Exploration in Asynchronous Federated Bandits

Zichen Wang, Chuanhao Li, Chenyu Song +3

We study the federated pure exploration problem of multi-armed bandits and linear bandits, where agents cooperatively identify the best arm via communicating with the central s…

cs.GT2023★ 4 cited

Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?

Fan Yao, Chuanhao Li, Karthik Abinav Sankararaman +5

The past decade has witnessed the flourishing of a new profession as media content creators, who rely on revenue streams from online content recommendation platforms. The reward me…

cs.GT2023★ 3 cited

How Bad is Top- Recommendation under Competing Content Creators?

Fan Yao, Chuanhao Li, Denis Nekipelov +2

Content creators compete for exposure on recommendation platforms, and such strategic behavior leads to a dynamic shift over the content distribution. However, how the creators' co…