Outlier detection is an important topic in machine learning and has been used in a wide range of applications. In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution. However, due to the sparsity of data in high-dimensional space, a limited number of potential outliers may fail to provide sufficient information to assist the classifier in describing a boundary that can separate outliers from normal data effectively. To address this, we propose a novel Single-Objective Generative Adversarial Active Learning (SO-GAAL) method for outlier detection, which can directly generate informative potential outliers based on the mini-max game between a generator and a discriminator. Moreover, to prevent the generator from falling into the mode collapsing problem, the stop node of training should be determined when SO-GAAL is able to provide sufficient information. But without any prior information, it is extremely difficult for SO-GAAL. Therefore, we expand the network structure of SO-GAAL from a single generator to multiple generators with different objectives (MO-GAAL), which can generate a reasonable reference distribution for the whole dataset. We empirically compare the proposed approach with several state-of-the-art outlier detection methods on both synthetic and real-world datasets. The results show that MO-GAAL outperforms its competitors in the majority of cases, especially for datasets with various cluster types or high irrelevant variable ratio. The experiment codes are available at: https://github.com/leibinghe/GAAL-based-outlier-detection

Purpose: Course recommendations are important for improving learner satisfaction and reducing dropout rates on massive open online course (MOOC) platforms. This study aims to propose an interpretable method of analyzing students' learning behaviors and recommending MOOCs by integrating multiple data sources.Design/methodology/approach: The study proposes a deep learning method of recommending MOOCs to students based on a multi-attention mechanism comprising learning records attention, word-level review attention, sentence-level review attention and course description attention. The proposed model is validated using real-world data consisting of the learning records of 6,628 students for 1,789 courses and 65,155 reviews.Findings: The main contribution of this study is its exploration of multiple unstructured information using the proposed multi-attention network model. It provides an interpretable strategy for analyzing students' learning behaviors and conducting personalized MOOC recommendations.Practical implications: The findings suggest that MOOC platforms must fully utilize the information implied in course reviews to extract personalized learning preferences.Originality/value: This study is the first attempt to recommend MOOCs by exploring students' preferences in course reviews. The proposed multi-attention mechanism improves the interpretability of MOOC recommendations.

With the wide use of Location-Based Social Networks (LBSNs), predicting user friendship from online social relations and offline trajectory data is of great value to improve the platform service quality and user satisfaction. Existing methods mainly focus on some hand-crafted features or graph embedding models based on the user-location bipartite graph, which cannot precisely capture the latent mobility similarity for the majority of users who have no explicit co-visit behaviors and also fail to balance the tradeoff between social features and mobility features for friendship prediction. In this regard, we propose a dual subgraph-based pairwise graph neural network (DSGNN) for friendship prediction in LBSNs, which extracts a pairwise social subgraph and a trajectory subgraph to model the social proximity and mobility similarity, respectively. Specifically, to overcome the co-visit data sparsity, we design an entropy-based random walk to construct a location graph that captures the high-level correlation between locations. Based on this, we characterize the pairwise mobility similarity from trajectory level instead of location level, which is modeled by a graph neural network (GNN) on a labeled trajectory subgraph composed of the two trajectories of the target user pair. Besides, we also utilize another GNN to extract social proximity based on social subgraph of the target user pair. Finally, we propose a gate layer to adaptively balance the fusion of the social and mobility features for friendship prediction. We conduct extensive experiments on the real-world datasets and demonstrate the superiority of our approach, which outperforms other state-of-the-art methods. In particular, the comparative experiments on the trajectory level mobility similarity further validate the effectiveness of the designed trajectory subgraph-based method, which can extract predictive mobility features.