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Ranking is a crucial module using in the recommender system. In particular,the ranking module using in our YoungTao recommendation scenario is to providean ordered list of items to users, to maximize the click number throughout therecommendation session for each user. However, we found that the traditionalranking method for optimizing Click-Through rate(CTR) cannot address ourranking scenario well, since it completely ignores user leaving, and CTR is theoptimization goal for the one-step recommendation. To effectively undertake thepurpose of our ranking module, we propose a long-term optimization goal, namedas CTE (Click-Through quantity expectation), for explicitly taking the behaviorof user leaving into account. Based on CTE, we propose an effective modeltrained by reinforcement learning. Moreover, we build a simulation environmentfrom offline log data for estimating PBR and CTR. We conduct extensiveexperiments on offline datasets and an online e-commerce scenario in TaoBao.Experimental results show that our method can boost performance effectively

CTR is not Enough: a Novel Reinforcement Learning based Ranking Approach for Optimizing Session Clicks