Modern navigation services often provide multiple paths connecting the same source and destination for users to select. Hence, ranking such paths becomes increasingly important, which directly affects the service quality. We present PathRank, a data-driven framework for ranking paths based on historical trajectories using multi-task learning. If a trajectory used path P from source s to destination d, PathRank considers this as an evidence that P is preferred over all other paths from s to d. Thus, a path that is similar to P should have a larger ranking score than a path that is dissimilar to P. Based on this intuition, PathRank models path ranking as a regression problem, where each path is associated with a ranking score.
To enable PathRank, we first propose an effective method to generate a compact set of training data: for each trajectory, we generate a small set of diversified paths. Next, we propose a multi-task learning framework to solve the regression problem. In particular, a spatial network embedding is proposed to embed each vertex to a feature vector by considering both road network topology and spatial properties, such as distances and travel times. Since a path is represented by a sequence of vertices, which is now a sequence of feature vectors after embedding, recurrent neural network is applied to model the sequence. The objective function is designed to consider errors on both ranking scores and spatial properties, making the framework a multi-task learning framework. Empirical studies on a substantial trajectory data set offer insight into the designed properties of the proposed framework and indicating that it is effective and practical.
Guiding Graph Embeddings using Path-Ranking Methods for Error Detection innoisy Knowledge Graphs
Institute of Informatics and Telecommunications, National Center Scientific Research Demokritos（德谟克利特国家科学研究中心信息与电信研究所）；Department of Informatics and Telecommunications, National and Kapodistrian University of Athens（雅典大学信息与电信学院）
Force without transmission: a depth-induced rank collapse that no loss on the representation reopens
Technische Universität Ilmenau（伊尔默瑙工业大学）；German Centre for Integrative Biodiversity Research (iDiv) Halle–Jena–Leipzig（德国综合生物多样性研究中心（iDiv）哈勒-耶拿-莱比锡）；Friedrich Schiller University（弗里德里希·席勒大学）
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI
Microsoft Research Asia（微软亚洲研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）；Hong Kong University of Science and Technology（香港科技大学）
