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Reinforcement learning (RL) has recently been introduced to interactiverecommender systems (IRS) because of its nature of learning from dynamicinteractions and planning for long-run performance. As IRS is always withthousands of items to recommend (i.e., thousands of actions), most existingRL-based methods, however, fail to handle such a large discrete action spaceproblem and thus become inefficient. The existing work that tries to deal withthe large discrete action space problem by utilizing the deep deterministicpolicy gradient framework suffers from the inconsistency between the continuousaction representation (the output of the actor network) and the real discreteaction. To avoid such inconsistency and achieve high efficiency andrecommendation effectiveness, in this paper, we propose a Tree-structuredPolicy Gradient Recommendation (TPGR) framework, where a balanced hierarchicalclustering tree is built over the items and picking an item is formulated asseeking a path from the root to a certain leaf of the tree. Extensiveexperiments on carefully-designed environments based on two real-world datasetsdemonstrate that our model provides superior recommendation performance andsignificant efficiency improvement over state-of-the-art methods.

Large-scale Interactive Recommendation with Tree-structured Policy Gradient