OpenRaiser/Intern-Atlas huggingface dataset:
Question headline generation for news articles
Authors: Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Jun Xu, Huanhuan Cao, Xueqi Cheng
Venue: Conference CIKM 2018
Year: 2018

In this paper, we introduce and tackle the Question Headline Generation (QHG) task. The motivation comes from the investigation of a real-world news portal where we find that news articles with question headlines often receive much higher click-through ratio than those with non-question headlines. The QHG task can be viewed as a specific form of the Question Generation (QG) task, with the emphasis on creating a natural question from a given news article by taking the entire article as the answer. A good QHG model thus should be able to generate a question by summarizing the essential topics of an article. Based on this idea, we propose a novel dual-attention sequence-to-sequence model (DASeq2Seq) for the QHG task. Unlike traditional sequence-to-sequence models which only employ the attention mechanism in the decoding phase for better generation, our DASeq2Seq further introduces a self-attention mechanism in the encoding phase to help generate a good summary of the article. We investigate two ways of the self-attention mechanism, namely global self-attention and distributed self-attention. Besides, we employ a vocabulary gate over both generic and question vocabularies to better capture the question patterns. Through the offline experiments, we show that our approach can significantly outperform the state-of-the-art question generation or headline generation models. Furthermore, we also conduct online evaluation to demonstrate the effectiveness of our approach using A/B test.
A Fine-Grained Taxonomy of Tables on the Web
Authors: Eric Crestan, Patrick Pantel
Venue: Conference CIKM 2010
Year: 2010

We propose a classification taxonomy over a large crawl of HTML tables on the Web, focusing primarily on those tables that express structured knowledge. The taxonomy separates tables into two top-level classes: a) those used for layout purposes, including navigational and formatting; and b) those containing relational knowledge, including listings, attribute/value, matrix, enumeration, and form. We then propose a classification algorithm for automatically detecting a subset of the classes in our taxonomy, namely layout tables and attribute/value tables. We report on the performance of our system over a large sample of manually annotated HTML tables on the Web.
DeExcelerator: a framework for extracting relational data from partially structured documents
Authors: Julian Eberius, Christopher Werner, Maik Thiele, Katrin Braunschweig, Lars Dannecker, Wolfgang Lehner
Venue: Conference CIKM 2013
Year: 2013

Of the structured data published on the web, for instance as datasets on Open Data Platforms such as data.gov, but also in the form of HTML tables on the general web, only a small part is in a relational form. Instead the data is intermingled with formatting, layout and textual metadata, i.e., it is contained in partially structured documents. This makes transformation into a true relational form necessary, which is a precondition for most forms of data analysis and data integration. Studying data.gov as an example source for partially structured documents, we present a classification of typical normalization problems. We then present the DeExcelerator, which is a framework for extracting relations from partially structured documents such as spreadsheets and HTML tables.
News recommendation via hypergraph learning: encapsulation of user behavior and news content
Authors: Lei Li, Tao Li
Venue: Conference WSDM 2013
Year: 2013

Personalized news recommender systems have gained increasing attention in recent years. Within a news reading community, the implicit correlations among news readers, news articles, topics and named entities, e.g., what types of named entities in articles are preferred by users, and why users like the articles, could be valuable for building an effective news recommender. In this paper, we propose a novel news personalization framework by mining such correlations. We use hypergraph to model various high-order relations among different objects in news data, and formulate news recommendation as a ranking problem on fine-grained hypergraphs. In addition, by transductive inference, our proposed algorithm is capable of effectively handling the so-called cold-start problem. Extensive experiments on a data set collected from various news websites have demonstrated the effectiveness of our proposed algorithm.
Predicting economic development using geolocated wikipedia articles
Authors: Evan Sheehan, Chenlin Meng, Matthew Tan, Burak Uzkent, Neal Jean, Marshall Burke, David B. Lobell, Stefano Ermon
Venue: Conference KDD 2019
Year: 2019

Progress on the UN Sustainable Development Goals (SDGs) is hampered by a persistent lack of data regarding key social, environmental, and economic indicators, particularly in developing countries. For example, data on poverty --- the first of seventeen SDGs --- is both spatially sparse and infrequently collected in Sub-Saharan Africa due to the high cost of surveys. Here we propose a novel method for estimating socioeconomic indicators using open-source, geolocated textual information from Wikipedia articles. We demonstrate that modern NLP techniques can be used to predict community-level asset wealth and education outcomes using nearby geolocated Wikipedia articles. When paired with nightlights satellite imagery, our method outperforms all previously published benchmarks for this prediction task, indicating the potential of Wikipedia to inform both research in the social sciences and future policy decisions.
Dynamic graph convolutional networks for entity linking
Authors: Junshuang Wu, Richong Zhang, Yongyi Mao, Hongyu Guo, Masoumeh Soflaei, Jinpeng Huai
Venue: Conference WWW 2020
Year: 2020

Entity linking, which maps named entity mentions in a document into the proper entities in a given knowledge graph, has been shown to be able to significantly benefit from modeling the entity relatedness through Graph Convolutional Networks (GCN). Nevertheless, existing GCN entity linking models fail to take into account the fact that the structured graph for a set of entities not only depends on the contextual information of the given document but also adaptively changes on different aggregation layers of the GCN, resulting in insufficiency in terms of capturing the structural information among entities. In this paper, we propose a dynamic GCN architecture to effectively cope with this challenge. The graph structure in our model is dynamically computed and modified during training. Through aggregating knowledge from dynamically linked nodes, our GCN model can collectively identify the entity mappings between the document and the knowledge graph, and efficiently capture the topical coherence among various entity mentions in the entire document. Empirical studies on benchmark entity linking data sets confirm the superior performance of our proposed strategy and the benefits of the dynamic graph structure.
Differentially private regression for discrete-time survival analysis
Authors: Thông T. Nguyên, Siu Cheung Hui
Venue: Conference CIKM 2017
Year: 2017

In survival analysis, regression models are used to understand the effects of explanatory variables (e.g., age, sex, weight, etc.) to the survival probability. However, for sensitive survival data such as medical data, there are serious concerns about the privacy of individuals in the data set when medical data is used to fit the regression models. The closest work addressing such privacy concerns is the work on Cox regression which linearly projects the original data to a lower dimensional space. However, the weakness of this approach is that there is no formal privacy guarantee for such projection. In this work, we aim to propose solutions for the regression problem in survival analysis with the protection of differential privacy which is a golden standard of privacy protection in data privacy research. To this end, we extend the Output Perturbation and Objective Perturbation approaches which are originally proposed to protect differential privacy for the Empirical Risk Minimization (ERM) problems. In addition, we also propose a novel sampling approach based on the Markov Chain Monte Carlo (MCMC) method to practically guarantee differential privacy with better accuracy. We show that our proposed approaches achieve good accuracy as compared to the non-private results while guaranteeing differential privacy for individuals in the private data set.
Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics
Authors: Chao Huang, Xian Wu, Xuchao Zhang, Chuxu Zhang, Jiashu Zhao, Dawei Yin, Nitesh V. Chawla
Venue: Conference KDD 2019
Year: 2019

Online purchase forecasting is of great importance in e-commerce platforms, which is the basis of how to present personalized interesting product lists to individual customers. However, predicting online purchases is not trivial as it is influenced by many factors including: (i) the complex temporal pattern with hierarchical inter-correlations; (ii) arbitrary category dependencies. To address these factors, we develop a Graph Multi-Scale Pyramid Networks (GMP) framework to fully exploit users' latent behavioral patterns with both multi-scale temporal dynamics and arbitrary inter-dependencies among product categories. In GMP, we first design a multi-scale pyramid modulation network architecture which seamlessly preserves the underlying hierarchical temporal factors--governing users' purchase behaviors. Then, we employ convolution recurrent neural network to encode the categorical temporal pattern at each scale. After that, we develop a resolution-wise recalibration gating mechanism to automatically re-weight the importance of each scale-view representations. Finally, a context-graph neural network module is proposed to adaptively uncover complex dependencies among category-specific purchases. Extensive experiments on real-world e-commerce datasets demonstrate the superior performance of our method over state-of-the-art baselines across various settings.
Attentive recurrent social recommendation
Authors: Peijie Sun, Le Wu, Meng Wang
Venue: Conference SIGIR 2018
Year: 2018

Collaborative filtering(CF) is one of the most popular techniques for building recommender systems. To alleviate the data sparsity issue in CF, social recommendation has emerged by leveraging social influence among users for better recommendation performance. In these systems, users' preferences over time are determined by their temporal dynamic interests as well as the general static interests. In the meantime, the complex interplay between users' internal interests and the social influence from the social network drives the evolution of users' preferences over time. Nevertheless, traditional approaches either neglected the social network structure for temporal recommendation or assumed a static social influence strength for static social recommendation. Thus, the problem of how to leverage social influence to enhance temporal social recommendation performance remains pretty much open. To this end, in this paper, we present an attentive recurrent network based approach for temporal social recommendation. In the proposed approach, we model users' complex dynamic and general static preferences over time by fusing social influence among users with two attention networks. Specifically, in the dynamic preference modeling process, we design a dynamic social aware recurrent neural network to capture users' complex latent interests over time, where a temporal attention network is proposed to learn the temporal social influence over time. In the general static preference modeling process, we characterize each user's static interest by introducing a static social attention network to model the stationary social influence among users. The output of the dynamic preferences and the static preferences are combined together in a unified end-to-end framework for the temporal social recommendation task. Finally, experimental results on two real-world datasets clearly show the superiority of our proposed model compared to the baselines.
OpenRec: A Modular Framework for Extensible and Adaptable Recommendation Algorithms
Authors: Longqi Yang, Eugene Bagdasaryan, Joshua Gruenstein, Cheng-Kang Hsieh, Deborah Estrin
Venue: Conference WSDM 2018
Year: 2018

With the increasing demand for deeper understanding of users» preferences, recommender systems have gone beyond simple user-item filtering and are increasingly sophisticated, comprised of multiple components for analyzing and fusing diverse information. Unfortunately, existing frameworks do not adequately support extensibility and adaptability and consequently pose significant challenges to rapid, iterative, and systematic, experimentation. In this work, we propose OpenRec, an open and modular Python framework that supports extensible and adaptable research in recommender systems. Each recommender is modeled as a computational graph that consists of a structured ensemble of reusable modules connected through a set of well-defined interfaces. We present the architecture of OpenRec and demonstrate that OpenRec provides adaptability, modularity and reusability while maintaining training efficiency and recommendation accuracy. Our case study illustrates how OpenRec can support an efficient design process to prototype and benchmark alternative approaches with inter-changeable modules and enable development and evaluation of new algorithms.
Autoblock: A hands-off blocking framework for entity matching
Authors: Wei Zhang, Hao Wei, Bunyamin Sisman, Xin Luna Dong, Christos Faloutsos, David Page
Venue: Conference WSDM 2020
Year: 2020

Entity matching seeks to identify data records over one or multiple data sources that refer to the same real-world entity. Virtually every entity matching task on large datasets requires blocking, a step that reduces the number of record pairs to be matched. However, most of the traditional blocking methods are learning-free and key-based, and their successes are largely built on laborious human effort in cleaning data and designing blocking keys. In this paper, we propose AutoBlock, a novel hands-off blocking framework for entity matching, based on similarity-preserving representation learning and nearest neighbor search. Our contributions include: (a) Automation: AutoBlock frees users from laborious data cleaning and blocking key tuning. (b) Scalability: AutoBlock has a sub-quadratic total time complexity and can be easily deployed for millions of records. (c) Effectiveness: AutoBlock outperforms a wide range of competitive baselines on multiple large-scale, real-world datasets, especially when datasets are dirty and/or unstructured.
Privacy preserving frequency capping in internet banner advertising
Authors: Ayman Farahat
Venue: Conference WWW 2009
Year: 2009

We describe an optimize-and-dispatch approach for delivering pay-per-impression advertisements in online advertising. The platform provider for an advertising network commits to showing advertisers' banner ads while capping the number of advertising message shown to a unique user as the user transitions through the network. The traditional approach for enforcing frequency caps has been to use cross-site cookies to track users. However,cross-site cookies and other tracking mechanisms can infringe on the user privacy. In this paper, we propose a novel linear programming approach that decides when to show an ad to the user based solely on the page currently viewed by the users. We show that the frequency caps are fulfilled in expectation. We show the efficacy of that approach using simulation results.
Extreme regression for dynamic search advertising
Authors: Yashoteja Prabhu, Aditya Kusupati, Nilesh Gupta, Manik Varma
Venue: Conference WSDM 2020
Year: 2020

This paper introduces a new learning paradigm called eXtreme Regression (XR) whose objective is to accurately predict the numerical degrees of relevance of an extremely large number of labels to a data point. XR can provide elegant solutions to many large-scale ranking and recommendation applications including Dynamic Search Advertising (DSA). XR can learn more accurate models than the recently popular extreme classifiers which incorrectly assume strictly binary-valued label relevances. Traditional regression metrics which sum the errors over all the labels are unsuitable for XR problems since they could give extremely loose bounds for the label ranking quality. Also, the existing regression algorithms won't efficiently scale to millions of labels. This paper addresses these limitations through: (1) new evaluation metrics for XR which sum only the k largest regression errors; (2) a new algorithm called XReg which decomposes XR task into a hierarchy of much smaller regression problems thus leading to highly efficient training and prediction. This paper also introduces a (3) new labelwise prediction algorithm in XReg useful for DSA and other recommendation tasks. Experiments on benchmark datasets demonstrated that XReg can outperform the state-of-the-art extreme classifiers as well as large-scale regressors and rankers by up to 50% reduction in the new XR error metric, and up to 2% and 2.4% improvements in terms of the propensity-scored precision metric used in extreme classification and the click-through rate metric used in DSA respectively. Deployment of XReg on DSA in Bing resulted in a relative gain of 58% in revenue and 27% in query coverage. XReg's source code can be downloaded from http://manikvarma.org/code/Xreg/download.html.
Personalized click shaping through lagrangian duality for online recommendation
Authors: Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, Xuanhui Wang
Venue: Conference SIGIR 2012
Year: 2012

Online content recommendation aims to identify trendy articles in a continuously changing dynamic content pool. Most of existing works rely on online user feedback, notably clicks, as the objective and maximize it by showing articles with highest click-through rates. Recently, click shaping was introduced to incorporate multiple objectives in a constrained optimization framework. The work showed that significant tradeoff among the competing objectives can be observed and thus it is important to consider multiple objectives. However, the proposed click shaping approach is segment-based and can only work with a few non-overlapping user segments. It remains a challenge of how to enable deep personalization in click shaping. In this paper, we tackle the challenge by proposing personalized click shaping. The main idea is to work with the Lagrangian duality formulation and explore strong convexity to connect dual and primal solutions. We show that our formulation not only allows efficient conversion from dual to primal for online personalized serving, but also enables us to solve the optimization faster by approximation. We conduct extensive experiments on a large real data set and our experimental results show that the personalized click shaping can significantly outperform the segmented one, while achieving the same ability to balance competing objectives.
Scalable noise mining in long-term electrocardiographic time series to predict death following heart attacks
Authors: Chih-Chun Chia, Zeeshan Syed
Venue: Conference KDD 2014
Year: 2014

Cardiac disease is the leading cause of death around the world; with ischemic heart disease alone claiming 7 million lives in 2011. This burden can be attributed, in part, to the absence of biomarkers that can reliably identify high risk patients and match them to treatments that are appropriate for them. In recent clinical studies, we have demonstrated the ability of computation to extract information with substantial prognostic utility that is typically disregarded in time-series data collected from cardiac patients. Of particular interest are subtle variations in long-term electrocardiographic (ECG) data that are usually overlooked as noise but provide a useful assessment of myocardial instability. In multiple clinical cohorts, we have developed the pathophysiological basis for studying probabilistic variations in long-term ECG and demonstrated the ability of this information to effectively risk stratify patients at risk of dying following heart attacks. In this paper, we extend this work and focus on the question of how to reduce its computational complexity for scalable use in large datasets or energy constrained embedded devices. Our basic approach to uncovering pathological structure within the ECG focuses on characterizing beat-to-beat time-warped shape deformations of the ECG using a modified dynamic time-warping (DTW) and Lomb-Scargle periodogram-based algorithm. As part of our efforts to scale this work up, we explore a novel approach to address the quadratic runtime of DTW. We achieve this by developing the idea of adaptive downsampling to reduce the size of the inputs presented to DTW, and describe changes to the dynamic programming problem underlying DTW to exploit adaptively downsampled ECG signals. When evaluated on data from 765 patients in the DISPERSE2-TIMI33 trial, our results show that high morphologic variability is associated with an 8- to 9-fold increased risk of death within 90 days of a heart attack. Moreover, the use of adaptive downsampling with a modified DTW formulation achieves a 7- to almost 20-fold reduction in runtime relative to DTW, without a significant change in biomarker discrimination.
Joint community and structural hole spanner detection via harmonic modularity
Authors: Lifang He, Chun-Ta Lu, Jiaqi Ma, Jianping Cao, Linlin Shen, Philip S. Yu
Venue: Conference KDD 2016
Year: 2016

Detecting communities (or modular structures) and structural hole spanners, the nodes bridging different communities in a network, are two essential tasks in the realm of network analytics. Due to the topological nature of communities and structural hole spanners, these two tasks are naturally tangled with each other, while there has been little synergy between them. In this paper, we propose a novel harmonic modularity method to tackle both tasks simultaneously. Specifically, we apply a harmonic function to measure the smoothness of community structure and to obtain the community indicator. We then investigate the sparsity level of the interactions between communities, with particular emphasis on the nodes connecting to multiple communities, to discriminate the indicator of SH spanners and assist the community guidance. Extensive experiments on real-world networks demonstrate that our proposed method outperforms several state-of-the-art methods in the community detection task and also in the SH spanner identification task (even the methods that require the supervised community information). Furthermore, by removing the SH spanners spotted by our method, we show that the quality of other community detection methods can be further improved.
Fairness and discrimination in retrieval and recommendation
Authors: Michael D. Ekstrand, Robin Burke, Fernando Diaz
Venue: Conference SIGIR 2019
Year: 2019

Fairness and related concerns have become of increasing importance in a variety of AI and machine learning contexts. They are also highly relevant to information retrieval and related problems such as recommendation, as evidenced by the growing literature in SIGIR, FAT*, RecSys, and special sessions such as the FATREC workshop and the Fairness track at TREC 2019; however, translating algorithmic fairness constructs from classification, scoring, and even many ranking settings into information retrieval and recommendation scenarios is not a straightforward task. This tutorial will help to orient IR researchers to algorithmic fairness, understand how concepts do and do not translate from other settings, and provide an introduction to the growing literature on this topic.