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While personalization increases the utility of recommender systems, it alsobrings the issue of filter bubbles. E.g., if the system keeps exposing andrecommending the items that the user is interested in, it may also make theuser feel bored and less satisfied. Existing work studies filter bubbles instatic recommendation, where the effect of overexposure is hard to capture. Incontrast, we believe it is more meaningful to study the issue in interactiverecommendation and optimize long-term user satisfaction. Nevertheless, it isunrealistic to train the model online due to the high cost. As such, we have toleverage offline training data and disentangle the causal effect on usersatisfaction.  To achieve this goal, we propose a counterfactual interactive recommendersystem (CIRS) that augments offline reinforcement learning (offline RL) withcausal inference. The basic idea is to first learn a causal user model onhistorical data to capture the overexposure effect of items on usersatisfaction. It then uses the learned causal user model to help the planningof the RL policy. To conduct evaluation offline, we innovatively create anauthentic RL environment (KuaiEnv) based on a real-world fully observed userrating dataset. The experiments show the effectiveness of CIRS in burstingfilter bubbles and achieving long-term success in interactive recommendation.The implementation of CIRS is available viahttps://github.com/chongminggao/CIRS-codes.

CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System