Optimizing Ranking Algorithm in Recommender System via Deep Reinforcement Learning
Jianhua Han, Yong Hai Yu, Feng Liu, Ruiming Tang, Yuzhou Zhang · 2019
Recommender system, which attempts to narrow down selections for users based on their preference, plays a crucial role in many E-commerce platforms like Amazon and Taobao. Sponsored search can be regarded as a major revenue contributor of recommender system. The platform sorts the items by a ranking function and charges the advertisers for the users' positive feedback (e.g., click). However, traditional ranking strategies usually apply greedy ranking strategies (e.g.,pCTR*Bid), which considers the recommendation processes to be static and only focuses on the immediate reward. In practice, recommendation processes may be highly correlated with each other and cumulative reward needs to be taken into account. To address these issues, this paper redefines the ranking function and proposes a Deep Reinforcement learning based Ranking Strategy (DRRS) to maximize the cumulative reward of the platform. The experiments conducted on real-world datasets demonstrate the effectiveness of the proposed framework.