Long-Term Value of Exploration: Measurements, Findings and Algorithms
| Autor: | Su, Yi, Wang, Xiangyu, Le, Elaine Ya, Liu, Liang, Li, Yuening, Lu, Haokai, Lipshitz, Benjamin, Badam, Sriraj, Heldt, Lukasz, Bi, Shuchao, Chi, Ed, Goodrow, Cristos, Wu, Su-Lin, Baugher, Lexi, Chen, Minmin |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 11 pages, WSDM 2024 |
| Databáze: | arXiv |
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