Xiangyang Luo

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  • Joint Adaptive Neighbours and Metric Learning for Multi-view Subspace Clustering

    Due to the existence of various views or representations in many real-world data, multi-view learning has drawn much attention recently. Multi-view spectral clustering methods based on similarity matrixes or graphs are pretty popular. Generally, these algorithms learn informative graphs by directly utilizing original data. However, in the real-world applications, original data often contain noises and outliers that lead to unreliable graphs. In addition, different views may have different contributions to data clustering. In this paper, a novel Multiview Subspace Clustering method unifying Adaptive neighbours and Metric learning (MSCAM), is proposed to address the above problems. In this method, we use the subspace representations of different views to adaptively learn a consensus similarity matrix, uncovering the subspace structure and avoiding noisy nature of original data. For all views, we also learn different Mahalanobis matrixes that parameterize the squared distances and consider the contributions of different views. Further, we constrain the graph constructed by the similarity matrix to have exact c (c is the number of clusters) connected components. An iterative algorithm is developed to solve this optimization problem. Moreover, experiments on a synthetic dataset and different real-world datasets demonstrate the effectiveness of MSCAM.

    09/12/2017 ∙ by Nan Xu, et al. ∙ 0 share

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  • DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets

    In recent years, with the prevalence of social media and smart devices, people causally reveal their locations such as shops, hotels, and restaurants in their tweets. Recognizing and linking such fine-grained location mentions to well-defined location profiles are beneficial for retrieval and recommendation systems. Prior studies heavily rely on hand-crafted linguistic features. Recently, deep learning has shown its effectiveness in feature representation learning for many NLP tasks. In this paper, we propose DLocRL, a new Deep pipeline for fine-grained Location Recognition and Linking in tweets. DLocRL leverages representation learning, semantic composition, attention and gate mechanisms to exploit semantic context features for location recognition and linking. Furthermore, a novel post-processing strategy, named Geographical Pair Linking, is developed to improve the linking performance. In this way, DLocRL does not require hand-crafted features. The experimental results show the effectiveness of DLocRL on fine-grained location recognition and linking with a real-world Twitter dataset.

    01/21/2019 ∙ by Canwen Xu, et al. ∙ 0 share

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  • A Review-Driven Neural Model for Sequential Recommendation

    Writing review for a purchased item is a unique channel to express a user's opinion in E-Commerce. Recently, many deep learning based solutions have been proposed by exploiting user reviews for rating prediction. In contrast, there has been few attempt to enlist the semantic signals covered by user reviews for the task of collaborative filtering. In this paper, we propose a novel review-driven neural sequential recommendation model (named RNS) by considering users' intrinsic preference (long-term) and sequential patterns (short-term). In detail, RNS is devised to encode each user or item with the aspect-aware representations extracted from the reviews. Given a sequence of historical purchased items for a user, we devise a novel hierarchical attention over attention mechanism to capture sequential patterns at both union-level and individual-level. Extensive experiments on three real-world datasets of different domains demonstrate that RNS obtains significant performance improvement over uptodate state-of-the-art sequential recommendation models.

    07/01/2019 ∙ by Chenliang Li, et al. ∙ 0 share

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