PASTO: Strategic Parameter Optimization in Recommendation Systems -- Probabilistic is Better than Deterministic

• URL: http://arxiv.org/abs/2108.09076v1
• Date: Fri, 20 Aug 2021 09:02:58 GMT
• Title: PASTO: Strategic Parameter Optimization in Recommendation Systems -- Probabilistic is Better than Deterministic
• Authors: Weicong Ding, Hanlin Tang, Jingshuo Feng, Lei Yuan, Sen Yang, Guangxu Yang, Jie Zheng, Jing Wang, Qiang Su, Dong Zheng, Xuezhong Qiu, Yongqi Liu, Yuxuan Chen, Yang Liu, Chao Song, Dongying Kong, Kai Ren, Peng Jiang, Qiao Lian, Ji Liu
• Abstract summary: We show that a probabilistic strategic parameter regime can achieve better value compared to the standard regime of finding a single deterministic parameter. Our approach is applied in a popular social network platform with hundreds of millions of daily users.
• Score: 33.174973495620215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-world recommendation systems often consist of two phases. In the first phase, multiple predictive models produce the probability of different immediate user actions. In the second phase, these predictions are aggregated according to a set of 'strategic parameters' to meet a diverse set of business goals, such as longer user engagement, higher revenue potential, or more community/network interactions. In addition to building accurate predictive models, it is also crucial to optimize this set of 'strategic parameters' so that primary goals are optimized while secondary guardrails are not hurt. In this setting with multiple and constrained goals, this paper discovers that a probabilistic strategic parameter regime can achieve better value compared to the standard regime of finding a single deterministic parameter. The new probabilistic regime is to learn the best distribution over strategic parameter choices and sample one strategic parameter from the distribution when each user visits the platform. To pursue the optimal probabilistic solution, we formulate the problem into a stochastic compositional optimization problem, in which the unbiased stochastic gradient is unavailable. Our approach is applied in a popular social network platform with hundreds of millions of daily users and achieves +0.22% lift of user engagement in a recommendation task and +1.7% lift in revenue in an advertising optimization scenario comparing to using the best deterministic parameter strategy.

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