Compatibility Family Learning for Item Recommendation and Generation

Compatibility between items, such as clothes and shoes, is a major factor among customer's purchasing decisions. However, learning "compatibility" is challenging due to (1) broader notions of compatibility than those of similarity, (2) the asymmetric nature of compatibility, and (3) only a small set of compatible and incompatible items are observed. We propose an end-to-end trainable system to embed each item into a latent vector and project a query item into K compatible prototypes in the same space. These prototypes reflect the broad notions of compatibility. We refer to both the embedding and prototypes as "Compatibility Family". In our learned space, we introduce a novel Projected Compatibility Distance (PCD) function which is differentiable and ensures diversity by aiming for at least one prototype to be close to a compatible item, whereas none of the prototypes are close to an incompatible item. We evaluate our system on a toy dataset, two Amazon product datasets, and Polyvore outfit dataset. Our method consistently achieves state-of-the-art performance. Finally, we show that we can visualize the candidate compatible prototypes using a Metric-regularized Conditional Generative Adversarial Network (MrCGAN), where the input is a projected prototype and the output is a generated image of a compatible item. We ask human evaluators to judge the relative compatibility between our generated images and images generated by CGANs conditioned directly on query items. Our generated images are significantly preferred, with roughly twice the number of votes as others.Comment: 9 pages, accepted to AAAI 201

Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks

With the rapid development of fashion market, the customers' demands of customers for fashion recommendation are rising. In this paper, we aim to investigate a practical problem of fashion recommendation by answering the question "which item should we select to match with the given fashion items and form a compatible outfit". The key to this problem is to estimate the outfit compatibility. Previous works which focus on the compatibility of two items or represent an outfit as a sequence fail to make full use of the complex relations among items in an outfit. To remedy this, we propose to represent an outfit as a graph. In particular, we construct a Fashion Graph, where each node represents a category and each edge represents interaction between two categories. Accordingly, each outfit can be represented as a subgraph by putting items into their corresponding category nodes. To infer the outfit compatibility from such a graph, we propose Node-wise Graph Neural Networks (NGNN) which can better model node interactions and learn better node representations. In NGNN, the node interaction on each edge is different, which is determined by parameters correlated to the two connected nodes. An attention mechanism is utilized to calculate the outfit compatibility score with learned node representations. NGNN can not only be used to model outfit compatibility from visual or textual modality but also from multiple modalities. We conduct experiments on two tasks: (1) Fill-in-the-blank: suggesting an item that matches with existing components of outfit; (2) Compatibility prediction: predicting the compatibility scores of given outfits. Experimental results demonstrate the great superiority of our proposed method over others.Comment: 11 pages, accepted by the 2019 World Wide Web Conference (WWW-2019

Detection, Disambiguation, and Argument Identification of Chinese Discourse Connectives

篇章關係指文字單位間如何有邏輯的彼此關聯。 透過文章中的篇章結構分析，我們可以更了解文件的意義。 因此，篇章結構分析被應用在很多領域， 例如自然語言界面以及大規模的文件分析。 相對於英文的篇章語料集早就提供研究者使用，中文的大規模篇章資料集一直 到近年才終於被釋出。同時，中文的篇章結構分析有很多獨特的議題， 例如中文的篇章連接詞的種類較多，且常有多個不連續詞語組成的 多重連接詞，此外，中文的句子結構也更為複雜，使得正確辨識篇章結構更為困難。 篇章連接詞是用來辨識中文文章中篇章關係的重要線索，但由於連接詞 本身的歧義性讓辨識篇章連接詞本身成為一個挑戰議題。在本篇論文中， 我們研究與篇章連接詞的顯性篇章關係有關的四個議題： 第一，我們處理篇章連接詞的辨識，在文章中找出可能的篇章連接詞。 第二，我們探討篇章連接詞的構成詞語間的多重連結關係。第三，我們研究 每個篇章連接詞的篇章關係消歧。最後，我們辨識每個篇章連結詞的論元。 我們提出不同的特徵來訓練基於羅吉斯迴歸 (Logistic Regression) 演算法的分類器來識別正確的篇章連接詞，以及辨識其篇章關係的種類。 此外，我們也將每個可能的候選連接詞排序， 並利用一個貪婪的演算法 (greedy algorithm) 來解決連結詞的連結關係歧義性。 最後，我們將論元辨識視為一個序列標記問題 (sequence labeling problem)， 並利用條件隨機域 (Conditional Random Fields) 來找出論元的邊界。 除了顯性篇章關係外，未來隱性篇章關係也需要進一步的研究， 在這些元件的基礎上，建立一個完整的中文篇章結構分析器。Discourse relations represent how textual units logically connect with each other. Analyzing the discourse structure for texts could aid the understanding of the meaning behind paragraphs. There are many potential applications such as natural language interface and large-scale content-analysis. Although there are popular English discourse corpora for researchers, large-scale Chinese discourse corpora have not been available until recently. In addition, Chinese discourse analysis has many unique issues including the variety of discourse connectives, the common occurrences of parallel connectives, and the complex sentence structures. Discourse connectives are important clues for identifying discourse relations in Chinese texts. However, the ambiguity involved makes it a challenge to extract true connectives. In this thesis, we investigate four tasks regarding explicit discourse relations that are signaled by discourse connectives. Firstly, we deal with the extraction of explicit discourse connectives. Secondly, we investigate resolving linking ambiguities among connective components. Thirdly, we disambiguate the discourse relation type for each connective. Finally, we extract the arguments for each discourse connective. Several features are proposed to train Logistic Regression classifiers to disambiguate between discourse and non-discourse usages and the relation types for connectives. Additionally, we rank each connective candidate and develop a greedy algorithm to resolve linking ambiguities. Finally, the argument identification is formulated as a sequence labeling problem, and Conditional Random Fields are utilized to determine the argument boundaries. Besides explicit discourse relations, further investigation must be done to recognize implicit relations. Built upon these components, an end-to-end discourse parser for Chinese may be constructed in future studies

Building a Care Management and Guidance Security System for Assisting Patients with Cognitive Impairment

The care of dementia patients presents a large challenge for caregivers and family members. Whether it is at home or in institutional care, patients have problems with spatial and environmental cognition. It often leads to abnormal behaviors such as a route recognition problem, wandering, or even getting lost. These behaviors require caregivers to keep an eye on the movement of the cognitively impaired elderly and the safety of these movement processes, to avoid them approaching dangerous areas or leaving the care environment. This paper used qualitative research methods (i.e., participatory interviews, case studies, and contextual observation methods) in the demand exploration phase and quantitative research methods in the product’s technological verification phase. In this study, we implemented a three-stage service design process—demand exploration, demand definition, and design execution—to analyze the care status and route recognition obstacles of elders with dementia, to identify hidden needs as a turning point for new product innovations in care management and guidance security. This study summarizes six service needs for care management and guides the surveillance and safety of elders with dementia: (1) offering indoor user-centered guidance, (2) providing the instant location information of elders with dementia to caregivers, (3) landmarks setting, (4) assistance notification, (5) environmental route planning, (6) use of a wearable device as a guide for indoor route guidance. Based on the potential deficiencies and demands of observation, the care management and guidance security system (CMGSS) was designed. The experimental results show that the use of ultra-wide band positioning technology used in the indoor guiding system can accurately guide the behavior of patients to the right position, provide accurate information for caregivers, and record their daily behavior. The error range of this technology was not only within 42.42 cm in indoor static positioning but also within 55 cm in dynamic positioning, even where wall thickness was 18 cm. Although the device was designed for institutional care, it can also be applied to the management and care of general home-based patients

CD4+ T cells mediate the development of liver fibrosis in high fat diet-induced NAFLD in humanized mice

Non-alcoholic fatty liver disease (NAFLD) has been on a global rise. While animal models have rendered valuable insights to the pathogenesis of NAFLD, discrepancy with patient data still exists. Since non-alcoholic steatohepatitis (NASH) involves chronic inflammation, and CD4+ T cell infiltration of the liver is characteristic of NASH patients, we established and characterized a humanized mouse model to identify human-specific immune response(s) associated with NAFLD progression. Immunodeficient mice engrafted with human immune cells (HIL mice) were fed with high fat and high calorie (HFHC) or chow diet for 20 weeks. Liver histology and immune profile of HIL mice were analyzed and compared with patient data. HIL mice on HFHC diet developed steatosis, inflammation and fibrosis of the liver. Human CD4+ central and effector memory T cells increased within the liver and in the peripheral blood of our HIL mice, accompanied by marked up-regulation of pro-inflammatory cytokines (IL-17A and IFNγ). In vivo depletion of human CD4+ T cells in HIL mice reduced liver inflammation and fibrosis, but not steatosis. Our results highlight CD4+ memory T cell subsets as important drivers of NAFLD progression from steatosis to fibrosis and provides a humanized mouse model for pre-clinical evaluation of potential therapeutics.Agency for Science, Technology and Research (A*STAR)National Medical Research Council (NMRC)National Research Foundation (NRF)Published versionThis study was supported by the National Research Foundation Singapore Fellowship (NRF-NRFF2017-03), NRF-ISF joint grant (NRF2019-NRF-ISF003-3127), Ensemble of Multi-Disciplinary Systems and Integrated Omics for NAFLD (EMULSION) diagnostic and therapeutic discovery (H18/01/a0/017), Agency for Science, Technology and Research (A∗STAR), Gilead Sciences International Research Scholars Program in Liver Disease (to QC), National Natural Science Foundation of China (81970520), and National Medical Research Council – Clinician Scientist – Individual Research Grant (NMRC/CIRG/1427/2015)