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Sampled in Pairs and Driven by Text: A New Graph Embedding Framework 成对采样和文本驱动:一种新的图嵌入框架
The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313520
Liheng Chen, Yanru Qu, Zhenghui Wang, Lin Qiu, Weinan Zhang, Ken Chen, Shaodian Zhang, Yong Yu
{"title":"Sampled in Pairs and Driven by Text: A New Graph Embedding Framework","authors":"Liheng Chen, Yanru Qu, Zhenghui Wang, Lin Qiu, Weinan Zhang, Ken Chen, Shaodian Zhang, Yong Yu","doi":"10.1145/3308558.3313520","DOIUrl":"https://doi.org/10.1145/3308558.3313520","url":null,"abstract":"In graphs with rich texts, incorporating textual information with structural information would benefit constructing expressive graph embeddings. Among various graph embedding models, random walk (RW)-based is one of the most popular and successful groups. However, it is challenged by two issues when applied on graphs with rich texts: (i) sampling efficiency: deriving from the training objective of RW-based models (e.g., DeepWalk and node2vec), we show that RW-based models are likely to generate large amounts of redundant training samples due to three main drawbacks. (ii) text utilization: these models have difficulty in dealing with zero-shot scenarios where graph embedding models have to infer graph structures directly from texts. To solve these problems, we propose a novel framework, namely Text-driven Graph Embedding with Pairs Sampling (TGE-PS). TGE-PS uses Pairs Sampling (PS) to improve the sampling strategy of RW, being able to reduce ~ 99% training samples while preserving competitive performance. TGE-PS uses Text-driven Graph Embedding (TGE), an inductive graph embedding approach, to generate node embeddings from texts. Since each node contains rich texts, TGE is able to generate high-quality embeddings and provide reasonable predictions on existence of links to unseen nodes. We evaluate TGE-PS on several real-world datasets, and experiment results demonstrate that TGE-PS produces state-of-the-art results on both traditional and zero-shot link prediction tasks.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"239 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79302356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
The Illusion of Change: Correcting for Biases in Change Inference for Sparse, Societal-Scale Data 变化的错觉:对稀疏的社会尺度数据的变化推断的偏差纠正
The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313722
Gabriel Cadamuro, Ramya Korlakai Vinayak, J. Blumenstock, S. Kakade, Jacob N. Shapiro
{"title":"The Illusion of Change: Correcting for Biases in Change Inference for Sparse, Societal-Scale Data","authors":"Gabriel Cadamuro, Ramya Korlakai Vinayak, J. Blumenstock, S. Kakade, Jacob N. Shapiro","doi":"10.1145/3308558.3313722","DOIUrl":"https://doi.org/10.1145/3308558.3313722","url":null,"abstract":"Societal-scale data is playing an increasingly prominent role in social science research; examples from research on geopolitical events include questions on how emergency events impact the diffusion of information or how new policies change patterns of social interaction. Such research often draws critical inferences from observing how an exogenous event changes meaningful metrics like network degree or network entropy. However, as we show in this work, standard estimation methodologies make systematically incorrect inferences when the event also changes the sparsity of the data. To address this issue, we provide a general framework for inferring changes in social metrics when dealing with non-stationary sparsity. We propose a plug-in correction that can be applied to any estimator, including several recently proposed procedures. Using both simulated and real data, we demonstrate that the correction significantly improves the accuracy of the estimated change under a variety of plausible data generating processes. In particular, using a large dataset of calls from Afghanistan, we show that whereas traditional methods substantially overestimate the impact of a violent event on social diversity, the plug-in correction reveals the true response to be much more modest.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85620995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
With a Little Help from My Friends (and Their Friends): Influence Neighborhoods for Social Recommendations 从我的朋友(和他们的朋友)的一点帮助:影响社区的社会推荐
The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313745
Avni Gulati, M. Eirinaki
{"title":"With a Little Help from My Friends (and Their Friends): Influence Neighborhoods for Social Recommendations","authors":"Avni Gulati, M. Eirinaki","doi":"10.1145/3308558.3313745","DOIUrl":"https://doi.org/10.1145/3308558.3313745","url":null,"abstract":"Social recommendations have been a very intriguing domain for researchers in the past decade. The main premise is that the social network of a user can be leveraged to enhance the rating-based recommendation process. This has been achieved in various ways, and under different assumptions about the network characteristics, structure, and availability of other information (such as trust, content, etc.) In this work, we create neighborhoods of influence leveraging only the social graph structure. These are in turn introduced in the recommendation process both as a pre-processing step and as a social regularization factor of the matrix factorization algorithm. Our experimental evaluation using real-life datasets demonstrates the effectiveness of the proposed technique.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85731331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
A Hierarchical Attention Retrieval Model for Healthcare Question Answering 医疗保健问答的分层注意检索模型
The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313699
Ming Zhu, Aman Ahuja, Wei Wei, C. Reddy
{"title":"A Hierarchical Attention Retrieval Model for Healthcare Question Answering","authors":"Ming Zhu, Aman Ahuja, Wei Wei, C. Reddy","doi":"10.1145/3308558.3313699","DOIUrl":"https://doi.org/10.1145/3308558.3313699","url":null,"abstract":"The growth of the Web in recent years has resulted in the development of various online platforms that provide healthcare information services. These platforms contain an enormous amount of information, which could be beneficial for a large number of people. However, navigating through such knowledgebases to answer specific queries of healthcare consumers is a challenging task. A majority of such queries might be non-factoid in nature, and hence, traditional keyword-based retrieval models do not work well for such cases. Furthermore, in many scenarios, it might be desirable to get a short answer that sufficiently answers the query, instead of a long document with only a small amount of useful information. In this paper, we propose a neural network model for ranking documents for question answering in the healthcare domain. The proposed model uses a deep attention mechanism at word, sentence, and document levels, for efficient retrieval for both factoid and non-factoid queries, on documents of varied lengths. Specifically, the word-level cross-attention allows the model to identify words that might be most relevant for a query, and the hierarchical attention at sentence and document levels allows it to do effective retrieval on both long and short documents. We also construct a new large-scale healthcare question-answering dataset, which we use to evaluate our model. Experimental evaluation results against several state-of-the-art baselines show that our model outperforms the existing retrieval techniques.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87715922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 39
From Small-scale to Large-scale Text Classification 从小规模到大规模文本分类
The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313563
Kang-Min Kim, Yeachan Kim, Jungho Lee, Ji-Min Lee, SangKeun Lee
{"title":"From Small-scale to Large-scale Text Classification","authors":"Kang-Min Kim, Yeachan Kim, Jungho Lee, Ji-Min Lee, SangKeun Lee","doi":"10.1145/3308558.3313563","DOIUrl":"https://doi.org/10.1145/3308558.3313563","url":null,"abstract":"Neural network models have achieved impressive results in the field of text classification. However, existing approaches often suffer from insufficient training data in a large-scale text classification involving a large number of categories (e.g., several thousands of categories). Several neural network models have utilized multi-task learning to overcome the limited amount of training data. However, these approaches are also limited to small-scale text classification. In this paper, we propose a novel neural network-based multi-task learning framework for large-scale text classification. To this end, we first treat the different scales of text classification (i.e., large and small numbers of categories) as multiple, related tasks. Then, we train the proposed neural network, which learns small- and large-scale text classification tasks simultaneously. In particular, we further enhance this multi-task learning architecture by using a gate mechanism, which controls the flow of features between the small- and large-scale text classification tasks. Experimental results clearly show that our proposed model improves the performance of the large-scale text classification task with the help of the small-scale text classification task. The proposed scheme exhibits significant improvements of as much as 14% and 5% in terms of micro-averaging and macro-averaging F1-score, respectively, over state-of-the-art techniques.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88749187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
Bridging Screen Readers and Voice Assistants for Enhanced Eyes-Free Web Search 桥接屏幕阅读器和语音助手增强眼睛自由的网络搜索
The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3314136
Alexandra Vtyurina, Adam Fourney, M. Morris, Leah Findlater, Ryen W. White
{"title":"Bridging Screen Readers and Voice Assistants for Enhanced Eyes-Free Web Search","authors":"Alexandra Vtyurina, Adam Fourney, M. Morris, Leah Findlater, Ryen W. White","doi":"10.1145/3308558.3314136","DOIUrl":"https://doi.org/10.1145/3308558.3314136","url":null,"abstract":"People with visual impairments often rely on screen readers when interacting with computer systems. Increasingly, these individuals also make extensive use of voice-based virtual assistants (VAs). We conducted a survey of 53 people who are legally blind to identify the strengths and weaknesses of both technologies, as well as the unmet opportunities at their intersection. We learned that virtual assistants are convenient and accessible, but lack the ability to deeply engage with content (e.g., read beyond the first few sentences of Wikipedia), and the ability to get a quick overview of the landscape (list alternative search results & suggestions). In contrast, screen readers allow for deep engagement with content (when content is accessible), and provide fine-grained navigation & control, but at the cost of increased complexity, and reduced walk-up-and-use convenience. In this demonstration, we showcase VERSE, a system that combines the positive aspects of VAs and screen readers, and allows other devices (e.g., smart watches) to serve as optional input accelerators. Together, these features allow people with visual impairments to deeply engage with web content through voice interaction.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"88 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91416613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 40
Pcard: Personalized Restaurants Recommendation from Card Payment Transaction Records Pcard:从信用卡支付交易记录中个性化推荐餐厅
The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313494
Min Du, Robert Christensen, Wei Zhang, Feifei Li
{"title":"Pcard: Personalized Restaurants Recommendation from Card Payment Transaction Records","authors":"Min Du, Robert Christensen, Wei Zhang, Feifei Li","doi":"10.1145/3308558.3313494","DOIUrl":"https://doi.org/10.1145/3308558.3313494","url":null,"abstract":"Personalized Point of Interest (POI) recommendation that incorporates users' personal preferences is an important subject of research. However, challenges exist such as dealing with sparse rating data and spatial location factors. As one of the biggest card payment organizations in the United States, our company holds abundant card payment transaction records with numerous features. In this paper, using restaurant recommendation as a demonstrating example, we present a personalized POI recommendation system (Pcard) that learns user preferences based on user transaction history and restaurants' locations. With a novel embedding approach that captures user embeddings and restaurant embeddings, we model pairwise restaurant preferences with respect to each user based on their locations and dining histories. Finally, a ranking list of restaurants within a spatial region is presented to the user. The evaluation results show that the proposed approach is able to achieve high accuracy and present effective recommendations.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91079781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 13
Evaluating Neural Text Simplification in the Medical Domain 评价医学领域的神经文本简化
The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313630
Laurens Van den Bercken, Robert-Jan Sips, C. Lofi
{"title":"Evaluating Neural Text Simplification in the Medical Domain","authors":"Laurens Van den Bercken, Robert-Jan Sips, C. Lofi","doi":"10.1145/3308558.3313630","DOIUrl":"https://doi.org/10.1145/3308558.3313630","url":null,"abstract":"Health literacy, i.e. the ability to read and understand medical text, is a relevant component of public health. Unfortunately, many medical texts are hard to grasp by the general population as they are targeted at highly-skilled professionals and use complex language and domain-specific terms. Here, automatic text simplification making text commonly understandable would be very beneficial. However, research and development into medical text simplification is hindered by the lack of openly available training and test corpora which contain complex medical sentences and their aligned simplified versions. In this paper, we introduce such a dataset to aid medical text simplification research. The dataset is created by filtering aligned health sentences using expert knowledge from an existing aligned corpus and a novel simple, language independent monolingual text alignment method. Furthermore, we use the dataset to train a state-of-the-art neural machine translation model, and compare it to a model trained on a general simplification dataset using an automatic evaluation, and an extensive human-expert evaluation.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91089628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 48
Learning Clusters through Information Diffusion 通过信息扩散学习集群
The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313560
L. Ostroumova, Alexey Tikhonov, N. Litvak
{"title":"Learning Clusters through Information Diffusion","authors":"L. Ostroumova, Alexey Tikhonov, N. Litvak","doi":"10.1145/3308558.3313560","DOIUrl":"https://doi.org/10.1145/3308558.3313560","url":null,"abstract":"When information or infectious diseases spread over a network, in many practical cases, one can observe when nodes adopt information or become infected, but the underlying network is hidden. In this paper, we analyze the problem of finding communities of highly interconnected nodes, given only the infection times of nodes. We propose, analyze, and empirically compare several algorithms for this task. The most stable performance, that improves the current state-of-the-art, is obtained by our proposed heuristic approaches, that are agnostic to a particular graph structure and epidemic model.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"464 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91478411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 13
Learning Task-Specific City Region Partition 学习特定任务的城市区域划分
The World Wide Web Conference Pub Date : 2019-05-13 DOI: 10.1145/3308558.3313704
Hongjian Wang, P. Jenkins, Hua Wei, Fei Wu, Z. Li
{"title":"Learning Task-Specific City Region Partition","authors":"Hongjian Wang, P. Jenkins, Hua Wei, Fei Wu, Z. Li","doi":"10.1145/3308558.3313704","DOIUrl":"https://doi.org/10.1145/3308558.3313704","url":null,"abstract":"The proliferation of publicly accessible urban data provide new insights on various urban tasks. A frequently used approach is to treat each region as a data sample and build a model over all the regions to observe the correlations between urban features (e.g., demographics) and the target variable (e.g., crime count). To define regions, most existing studies use fixed grids or pre-defined administrative boundaries (e.g., census tracts or community areas). In reality, however, definitions of regions should be different depending on tasks (e.g., regional crime count prediction vs. real estate prices estimation). In this paper, we propose a new problem of task-specific city region partitioning, aiming to find the best partition in a city w.r.t. a given task. We prove this is an NP-hard search problem with no trivial solution. To learn the partition, we first study two variants of Markov Chain Monte Carlo (MCMC). We further propose a reinforcement learning scheme for effective sampling the search space. We conduct experiments on two real datasets in Chicago (i.e., crime count and real estate price) to demonstrate the effectiveness of our proposed method.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90349693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
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