MDP2 Forest: A Constrained Continuous Multi-dimensional Policy Optimization Approach for Short-video Recommendation

In the ecology of short video platforms, the optimal exposure proportion of each video category is crucial to guide recommendation systems and content production in a macroscopic way. Though extensive studies on recommendation systems are devoted to providing the most well-matched videos for each view request, fitting the data without considering inherent biases such as selection bias and exposure bias will result in serious issues. In this paper, we formalize the exposure proportion strategy as a policy-making problem with multi-dimensional continuous treatment under certain constraints from a causal inference point of view. We propose a novel ensemble policy learning method based on causal trees, called Maximum Difference of Preference Point Forest (MDP2 Forest), which overcomes the shortcomings of existing policy learning approaches. Experimental results on both simulated and synthetic datasets show the superiority of our algorithm compared to other policy learning or causal inference methods in terms of the treatment estimation accuracy and the mean regret. Furthermore, the proposed MDP2 Forest method can also adapt to a wide range of business settings such as imposing different kinds of constraints on the multi-dimensional treatment.