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

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

arXiv · 2305.07764

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

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

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Abstract

Effective exploration is believed to positively influence the long-term user experience on recommendation platforms. Determining its exact benefits, however, has been challenging. Regular A/B tests on exploration often measure neutral or even negative engagement metrics while failing to capture its long-term benefits. We here introduce new experiment designs to formally quantify the long-term value of exploration by examining its effects on content corpus, and connecting content corpus growth to the long-term user experience from real-world experiments. Once established the values of exploration, we investigate the Neural Linear Bandit algorithm as a general framework to introduce exploration into any deep learning based ranking systems. 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.

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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. 2023-05-12. Long-Term Value of Exploration: Measurements, Findings and Algorithms. https://arxiv.org/abs/2305.07764

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