DDEN: A Heterogeneous Learning-to-Rank Approach with Deep Debiasing Experts Network
Wenchao Xiu, Yiran Wang, Taofeng Xue, Kai Zhang, Qin Zhang, Zhonghuo Wu, Yifan Yang, Gong Zhang · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · 2022
Learning-to-Rank(LTR) is widely used in many Information Retrieval(IR) scenarios, including web search and Location Based Services(LBS) search. However, most existing LTR techniques mainly focus on homogeneous ranking. Taking QAC in Dianping search as an example, heterogeneous documents including suggested queries (SQ) and Point-of-Interests(POI) need to be ranked and presented to enhance user experience. New challenges are faced when conducting heterogeneous ranking, including inconsistent feature space and more serious position bias caused by distinct representation spaces. Therefore, we propose Deep Debiasing Experts Network (DDEN), a novel heterogeneous LTR approach based on Mixture-of-Experts architecture and gating network, to deal with the inconsistent feature space of documents in ranking system. Furthermore, DDEN mitigates the position bias by adopting adversarial-debiasing framework embedded with heterogeneous LTR techniques. We conduct reproducible experiments on industrial datasets from Dianping, one of the largest local life platforms, and deploy DDEN in online application. Results show that DDEN substantially improves ranking performance in offline evaluation and boost the overall click-through rate in online A/B test by 2.1%.