Balancing Utility and Exposure Fairness for Integrated Ranking with Reinforcement Learning
Wei Xia, Weiwen Liu, Yifan Liu, Ruiming Tang · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
Integrated ranking is critical in industrial recommendation systems and has attracted increasing attention. In an integrated ranking system, items from multiple channels are merged together and form an integrated list. During this process, apart from optimizing the system's utility like the total number of clicks, a fair allocation of the exposure opportunities over different channels also needs to be satisfied. To address this problem, we propose an integrated ranking model called Integrated Deep-Q Network (iDQN), which jointly considers user preferences, the platform's utility, and the exposure fairness. Extensive offline experiments validate the effectiveness of iDQN in managing the tradeoff between utility and fairness. Moreover, iDQN also has been deployed onto the online AppStore platform in Huawei, where the online A/B test shows iDQN outperforms the baseline by 1.87% and 2.21% in terms of utility and fairness, respectively.