LLMs for User Interest Exploration: A Hybrid Approach

no code implementations • 25 May 2024 • Jianling Wang, Haokai Lu, Yifan Liu, He Ma, Yueqi Wang, Yang Gu, Shuzhou Zhang, Ningren, Han, Shuchao Bi, Lexi Baugher, Ed Chi, Minmin Chen

Traditional recommendation systems are subject to a strong feedback loop by learning from and reinforcing past user-item interactions, which in turn limits the discovery of novel user interests.

Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation

no code implementations • 2 Jun 2023 • Jianling Wang, Haokai Lu, Sai Zhang, Bart Locanthi, HaoTing Wang, Dylan Greaves, Benjamin Lipshitz, Sriraj Badam, Ed H. Chi, Cristos Goodrow, Su-Lin Wu, Lexi Baugher, Minmin Chen

The multi-funnel setup effectively balances between coverage and relevance.

Long-Term Value of Exploration: Measurements, Findings and Algorithms

no code implementations • 12 May 2023 • Yi Su, Xiangyu Wang, Elaine Ya Le, Liang Liu, Yuening Li, Haokai Lu, Benjamin Lipshitz, Sriraj Badam, Lukasz Heldt, Shuchao Bi, Ed Chi, Cristos Goodrow, Su-Lin Wu, Lexi Baugher, Minmin Chen

We conduct live experiments on one of the largest short-form video recommendation platforms that serves billions of users to validate the new experiment designs, quantify the long-term values of exploration, and to verify the effectiveness of the adopted neural linear bandit algorithm for exploration.

Recommendation Systems