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Real-world recommendation systems often consist of two phases. In the firstphase, multiple predictive models produce the probability of differentimmediate user actions. In the second phase, these predictions are aggregatedaccording to a set of 'strategic parameters' to meet a diverse set of businessgoals, such as longer user engagement, higher revenue potential, or morecommunity/network interactions. In addition to building accurate predictivemodels, it is also crucial to optimize this set of 'strategic parameters' sothat primary goals are optimized while secondary guardrails are not hurt. Inthis setting with multiple and constrained goals, this paper discovers that aprobabilistic strategic parameter regime can achieve better value compared tothe standard regime of finding a single deterministic parameter. The newprobabilistic regime is to learn the best distribution over strategic parameterchoices and sample one strategic parameter from the distribution when each uservisits the platform. To pursue the optimal probabilistic solution, we formulatethe problem into a stochastic compositional optimization problem, in which theunbiased stochastic gradient is unavailable. Our approach is applied in apopular social network platform with hundreds of millions of daily users andachieves +0.22% lift of user engagement in a recommendation task and +1.7% liftin revenue in an advertising optimization scenario comparing to using the bestdeterministic parameter strategy.

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