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Long-term engagement is preferred over immediate engagement in sequentialrecommendation as it directly affects product operational metrics such as dailyactive users (DAUs) and dwell time. Meanwhile, reinforcement learning (RL) iswidely regarded as a promising framework for optimizing long-term engagement insequential recommendation. However, due to expensive online interactions, it isvery difficult for RL algorithms to perform state-action value estimation,exploration and feature extraction when optimizing long-term engagement. Inthis paper, we propose ResAct which seeks a policy that is close to, but betterthan, the online-serving policy. In this way, we can collect sufficient datanear the learned policy so that state-action values can be properly estimated,and there is no need to perform online exploration. ResAct optimizes the policyby first reconstructing the online behaviors and then improving it via aResidual Actor. To extract long-term information, ResAct utilizes twoinformation-theoretical regularizers to confirm the expressiveness andconciseness of features. We conduct experiments on a benchmark dataset and alarge-scale industrial dataset which consists of tens of millions ofrecommendation requests. Experimental results show that our methodsignificantly outperforms the state-of-the-art baselines in various long-termengagement optimization tasks.

ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor