Learning to Rank has traditionally considered settings where given the relevance information of objects, the desired order in which to rank the objects is clear. However, with today's large variety of users and layouts this is not always the case. In this paper, we consider so-called complex ranking settings where it is not clear what should be displayed, that is, what the relevant items are, and how they should be displayed, that is, where the most relevant items should be placed. These ranking settings are complex as they involve both traditional ranking and inferring the best display order. Existing learning to rank methods cannot handle such complex ranking settings as they assume that the display order is known beforehand. To address this gap we introduce a novel Deep Reinforcement Learning method that is capable of learning complex rankings, both the layout and the best ranking given the layout, from weak reward signals. Our proposed method does so by selecting documents and positions sequentially, hence it ranks both the documents and positions, which is why we call it the Double-Rank Model (DRM). Our experiments show that DRM outperforms all existing methods in complex ranking settings, thus it leads to substantial ranking improvements in cases where the display order is not known a priori.
Xinyu AI Search: Enhanced Relevance and Comprehensive Results with Rich Answer Presentations
AIDS and SIAR, University of Science and Technology of China（艾滋病与SIAR，中国科学技术大学）；ESAT-PSI, KU Leuven（ESAT-PSI，比利时鲁汶大学）；Institute for Advanced Algorithms Research（先进算法研究所）；The Hong Kong Polytechnic University（香港理工大学）
本文提出Xinyu AI Search，通过查询分解图、多源聚合检索和重排序优化，结合细粒度引用与可视化呈现，构建了首个整合检索、生成与用户中心呈现的生成式AI搜索综合框架。
Implicit feedback (e.g., click, dwell time) is an attractive source of training data for Learning-to-Rank, but its naive use leads to learning results that are distorted by presentation bias. For the special case of optimizing average rank for linear ranking functions, however, the recently developed SVM-PropRank method has shown that counterfactual inference techniques can be used to provably overcome the distorting effect of presentation bias. Going beyond this special case, this paper provides a general and theoretically rigorous framework for counterfactual learning-to-rank that enables un…
