OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic Recommendations for Sequential Hiring Decisions in Online Labor Markets
Authors: Marios Kokkodis
Venue: KDD
Year: 2018

Online labor markets facilitate transactions between employers and a diverse set of independent contractors around the globe. When making hiring decisions in these markets, employers have to assess a large and heterogeneous population of contractors. Because many of the contractors' characteristics are latent, employers often make risky decisions that end up in negative outcomes. In this work, we address this issue by proposing a framework for recommending contractors who are likely to get hired and successfully complete the task at hand. We start our analysis by acknowledging that employers' hiring behavior dynamically evolves with time; Employers learn to choose contractors according to the outcomes of their previously completed tasks. To capture this dynamic evolution, we propose a structured Hidden Markov Model that explicitly models task outcomes through the employers' evolution. We build and evaluate the proposed framework on a dataset of real online hiring decisions. We then compare our approach with a set of previously proposed static algorithms and we show that our proposed framework provides up to 24% improved recommendations. We conclude by discussing the positive impact that such better recommendations of candidates can have on employers, contractors, and the market itself.
A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization
Authors: Ching-pei Lee, Cong Han Lim, Stephen J. Wright
Venue: KDD
Year: 2018

We propose a communication- and computation-efficient distributed optimization algorithm using second-order information for solving ERM problems with a nonsmooth regularization term. Current second-order and quasi-Newton methods for this problem either do not work well in the distributed setting or work only for specific regularizers. Our algorithm uses successive quadratic approximations, and we describe how to maintain an approximation of the Hessian and solve subproblems efficiently in a distributed manner. The proposed method enjoys global linear convergence for a broad range of non-strongly convex problems that includes the most commonly used ERMs, thus requiring lower communication complexity. It also converges on non-convex problems, so has the potential to be used on applications such as deep learning. Initial computational results on convex problems demonstrate that our method significantly improves on communication cost and running time over the current state-of-the-art methods.
Winner's Curse: Bias Estimation for Total Effects of Features in Online Controlled Experiments
Authors: Minyong R. Lee, Milan Shen
Venue: KDD
Year: 2018

Online controlled experiments, or A/B testing, has been a standard framework adopted by most online product companies to measure the effect of any new change. Companies use various statistical methods including hypothesis testing and statistical inference to quantify the business impact of the changes and make product decisions. Nowadays, experimentation platforms can run as many as hundreds or even more experiments concurrently. When a group of experiments is conducted, usually the ones with significant successful results are chosen to be launched into the product. We are interested in learning the aggregated impact of the launched features. In this paper, we investigate a statistical selection bias in this process and propose a correction method of getting an unbiased estimator. Moreover, we give an implementation example at Airbnb's ERF platform (Experiment Reporting Framework) and discuss the best practices to account for this bias.
TruePIE: Discovering Reliable Patterns in Pattern-Based Information Extraction
Authors: Qi Li, Meng Jiang, Xikun Zhang, Meng Qu, Timothy P. Hanratty, Jing Gao, Jiawei Han
Venue: KDD
Year: 2018

Pattern-based methods have been successful in information extraction and NLP research. Previous approaches learn the quality of a textual pattern as relatedness to a certain task based on statistics of its individual content (e.g., length, frequency) and hundreds of carefully-annotated labels. However, patterns of good content-quality may generate heavily conflicting information due to the big gap between relatedness and correctness. Evaluating the correctness of information is critical in (entity, attribute, value)-tuple extraction. In this work, we propose a novel method, called TruePIE, that finds reliable patterns which can extract not only related but also correct information. TruePIE adopts the self-training framework and repeats the training-predicting-extracting process to gradually discover more and more reliable patterns. To better represent the textual patterns, pattern embeddings are formulated so that patterns with similar semantic meanings are embedded closely to each other. The embeddings jointly consider the local pattern information and the distributional information of the extractions. To conquer the challenge of lacking supervision on patterns' reliability, TruePIE can automatically generate high quality training patterns based on a couple of seed patterns by applying the arity-constraints to distinguish highly reliable patterns (i.e., positive patterns) and highly unreliable patterns (i.e., negative patterns). Experiments on a huge news dataset (over 25GB) demonstrate that the proposed TruePIE significantly outperforms baseline methods on each of the three tasks: reliable tuple extraction, reliable pattern extraction, and negative pattern extraction.
Multi-task Representation Learning for Travel Time Estimation
Authors: Yaguang Li, Kun Fu, Zheng Wang, Cyrus Shahabi, Jieping Ye, Yan Liu
Venue: KDD
Year: 2018

One crucial task in intelligent transportation systems is estimating the duration of a potential trip given the origin location, destination location as well as the departure time. Most existing approaches for travel time estimation assume that the route of the trip is given, which does not hold in real-world applications since the route can be dynamically changed due to traffic conditions, user preferences, etc. As inferring the path from the origin and the destination can be time-consuming and nevertheless error-prone, it is desirable to perform origin-destination travel time estimation, which aims to predict the travel time without online route information. This problem is challenging mainly due to its limited amount of information available and the complicated spatiotemporal dependency. In this paper, we propose a MUlti-task Representation learning model for Arrival Time estimation (MURAT). This model produces meaningful representation that preserves various trip properties in the real-world and at the same time leverages the underlying road network and the spatiotemporal prior knowledge. Further-more, we propose a multi-task learning framework to utilize the path information of historical trips during the training phase which boosts the performance. Experimental results on two large-scale real-world datasets show that the proposed approach achieves clear improvements over state-of-the-art methods
E-tail Product Return Prediction via Hypergraph-based Local Graph Cut
Authors: Jianbo Li, Jingrui He, Yada Zhu
Venue: KDD
Year: 2018

Recent decades have witnessed the rapid growth of E-commerce. In particular, E-tail has provided customers with great convenience by allowing them to purchase retail products anywhere without visiting the actual stores. A recent trend in E-tail is to allow free shipping and hassle-free returns to further attract online customers. However, a downside of such a customer-friendly policy is the rapidly increasing return rate as well as the associated costs of handling returned online orders. Therefore, it has become imperative to take proactive measures for reducing the return rate and the associated cost. Despite the large amount of data available from historical purchase and return records, up until now, the problem of E-tail product return prediction has not attracted much attention from the data mining community. To address this problem, in this paper, we propose a generic framework for E-tail product return prediction named HyperGo . It aims to predict the customer's intention to return after s/he has put together the shopping basket. For the baskets with a high return intention, the E-tailers can then take appropriate measures to incentivize the customer not to issue a return and/or prepare for reverse logistics. The proposed HyperGo is based on a novel hypergraph representation of historical purchase and return records, effectively leveraging the rich information of basket composition. For a given basket, we propose a local graph cut algorithm using truncated random walk on the hypergraph to identify similar historical baskets. Based on these baskets, HyperGo is able to estimate the return intention on two levels: basket-level vs. product-level, which provides the E-tailers with detailed information regarding the reason for a potential return (e.g., duplicate products with different colors). One major benefit of the proposed local algorithm lies in its time complexity, which is linearly dependent on the size of the output cluster and polylogarithmically dependent on the volume of the hypergraph. This makes HyperGo particularly suitable for processing large-scale data sets. The experimental results on multiple real-world E-tail data sets demonstrate the effectiveness and efficiency of HyperGo .
An Efficient Two-Layer Mechanism for Privacy-Preserving Truth Discovery
Authors: Yaliang Li, Chenglin Miao, Lu Su, Jing Gao, Qi Li, Bolin Ding, Zhan Qin, Kui Ren
Venue: KDD
Year: 2018

Soliciting answers from online users is an efficient and effective solution to many challenging tasks. Due to the variety in the quality of users, it is important to infer their ability to provide correct answers during aggregation. Therefore, truth discovery methods can be used to automatically capture the user quality and aggregate user-contributed answers via a weighted combination. Despite the fact that truth discovery is an effective tool for answer aggregation, existing work falls short of the protection towards the privacy of participating users. To fill this gap, we propose perturbation-based mechanisms that provide users with privacy guarantees and maintain the accuracy of aggregated answers. We first present a one-layer mechanism, in which all the users adopt the same probability to perturb their answers. Aggregation is then conducted on perturbed answers but the aggregation accuracy could drop accordingly. To improve the utility, a two-layer mechanism is proposed where users are allowed to sample their own probabilities from a hyper distribution. We theoretically compare the one-layer and two-layer mechanisms, and prove that they provide the same privacy guarantee while the two-layer mechanism delivers better utility. This advantage is brought by the fact that the two-layer mechanism can utilize the estimated user quality information from truth discovery to reduce the accuracy loss caused by perturbation, which is confirmed by experimental results on real-world datasets. Experimental results also demonstrate the effectiveness of the proposed two-layer mechanism in privacy protection with tolerable accuracy loss in aggregation.
TATC: Predicting Alzheimer's Disease with Actigraphy Data
Authors: Jia Li, Yu Rong, Helen Meng, Zhihui Lu, Timothy C. Y. Kwok, Hong Cheng
Venue: KDD
Year: 2018

With the increase of elderly population, Alzheimer's Disease (AD), as the most common cause of dementia among the elderly, is affecting more and more senior people. It is crucial for a patient to receive accurate and timely diagnosis of AD. Current diagnosis relies on doctors' experience and clinical test, which, unfortunately, may not be performed until noticeable AD symptoms are developed. In this work, we present our novel solution named time-aware TICC and CNN (TATC), for predicting AD from actigraphy data. TATC is a multivariate time series classification method using a neural attention-based deep learning approach. It not only performs accurate prediction of AD risk, but also generates meaningful interpretation of daily behavior pattern of subjects. TATC provides an automatic, low-cost solution for continuously monitoring the change of physical activity of subjects in daily living environment. We believe the future deployment of TATC can benefit both doctors and patients in early detection of potential AD risk.
Learning Adversarial Networks for Semi-Supervised Text Classification via Policy Gradient
Authors: Yan Li, Jieping Ye
Venue: KDD
Year: 2018

Semi-supervised learning is a branch of machine learning techniques that aims to make fully use of both labeled and unlabeled instances to improve the prediction performance. The size of modern real world datasets is ever-growing so that acquiring label information for them is extraordinarily difficult and costly. Therefore, deep semi-supervised learning is becoming more and more popular. Most of the existing deep semi-supervised learning methods are built under the generative model based scheme, where the data distribution is approximated via input data reconstruction. However, this scheme does not naturally work on discrete data, e.g., text; in addition, learning a good data representation is sometimes directly opposed to the goal of learning a high performance prediction model. To address the issues of this type of methods, we reformulate the semi-supervised learning as a model-based reinforcement learning problem and propose an adversarial networks based framework. The proposed framework contains two networks: a predictor network for target estimation and a judge network for evaluation. The judge network iteratively generates proper reward to guide the training of predictor network, and the predictor network is trained via policy gradient. Based on the aforementioned framework, we propose a recurrent neural network based model for semi-supervised text classification. We conduct comprehensive experimental analysis on several real world benchmark text datasets, and the results from our evaluations show that our method outperforms other competing state-of-the-art methods.
High-order Proximity Preserving Information Network Hashing
Authors: Defu Lian, Kai Zheng, Vincent W. Zheng, Yong Ge, Longbing Cao, Ivor W. Tsang, Xing Xie
Venue: KDD
Year: 2018

Information network embedding is an effective way for efficient graph analytics. However, it still faces with computational challenges in problems such as link prediction and node recommendation, particularly with increasing scale of networks. Hashing is a promising approach for accelerating these problems by orders of magnitude. However, no prior studies have been focused on seeking binary codes for information networks to preserve high-order proximity. Since matrix factorization (MF) unifies and outperforms several well-known embedding methods with high-order proximity preserved, we propose a MF-based \underlineI nformation \underlineN etwork \underlineH ashing (INH-MF) algorithm, to learn binary codes which can preserve high-order proximity. We also suggest Hamming subspace learning, which only updates partial binary codes each time, to scale up INH-MF. We finally evaluate INH-MF on four real-world information network datasets with respect to the tasks of node classification and node recommendation. The results demonstrate that INH-MF can perform significantly better than competing learning to hash baselines in both tasks, and surprisingly outperforms network embedding methods, including DeepWalk, LINE and NetMF, in the task of node recommendation. The source code of INH-MF is available online\footnote\urlhttps://github.com/DefuLian/network .
BigIN4: Instant, Interactive Insight Identification for Multi-Dimensional Big Data
Authors: Qingwei Lin, Weichen Ke, Jian-Guang Lou, Hongyu Zhang, Kaixin Sui, Yong Xu, Ziyi Zhou, Bo Qiao, Dongmei Zhang
Venue: KDD
Year: 2018

The ability to identify insights from multi-dimensional big data is important for business intelligence. To enable interactive identification of insights, a large number of dimension combinations need to be searched and a series of aggregation queries need to be quickly answered. The existing approaches answer interactive queries on big data through data cubes or approximate query processing. However, these approaches can hardly satisfy the performance or accuracy requirements for ad-hoc queries demanded by interactive exploration. In this paper, we present BigIN4, a system for instant, interactive identification of insights from multi-dimensional big data. BigIN4 gives insight suggestions by enumerating subspaces and answers queries by combining data cube and approximate query processing techniques. If a query cannot be answered by the cubes, BigIN4 decomposes it into several low dimensional queries that can be directly answered by the cubes through an online constructed Bayesian Network and gives an approximate answer within a statistical interval. Unlike the related works, BigIN4 does not require any prior knowledge of queries and does not assume a certain data distribution. Our experiments on ten real-world large-scale datasets show that BigIN4 can successfully identify insights from big data. Furthermore, BigIN4 can provide approximate answers to aggregation queries effectively (with less than 10% error on average) and efficiently (50x faster than sampling-based methods).
Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning
Authors: Kaixiang Lin, Renyu Zhao, Zhe Xu, Jiayu Zhou
Venue: KDD
Year: 2018

Large-scale online ride-sharing platforms have substantially transformed our lives by reallocating transportation resources to alleviate traffic congestion and promote transportation efficiency. An efficient fleet management strategy not only can significantly improve the utilization of transportation resources but also increase the revenue and customer satisfaction. It is a challenging task to design an effective fleet management strategy that can adapt to an environment involving complex dynamics between demand and supply. Existing studies usually work on a simplified problem setting that can hardly capture the complicated stochastic demand-supply variations in high-dimensional space. In this paper we propose to tackle the large-scale fleet management problem using reinforcement learning, and propose a contextual multi-agent reinforcement learning framework including two concrete algorithms, namely contextual deep Q-learning and contextual multi-agent actor-critic, to achieve explicit coordination among a large number of agents adaptive to different contexts. We show significant improvements of the proposed framework over state-of-the-art approaches through extensive empirical studies.
Lessons Learned from Developing and Deploying a Large-Scale Employer Name Normalization System for Online Recruitment
Authors: Qiaoling Liu, Josh Chao, Thomas Mahoney, Alan Chern, Chris Min, Faizan Javed, Valentin Jijkoun
Venue: KDD
Year: 2018

Employer name normalization, or linking employer names in job postings or resumes to entities in an employer knowledge base (KB), is important for many downstream applications in the online recruitment domain. Key challenges for employer name normalization include handling employer names from both job postings and resumes, leveraging the corresponding location and URL context, and handling name variations and duplicates in the KB. In this paper, we describe the CompanyDepot system developed at CareerBuilder, which uses machine learning techniques to address these challenges. We discuss the main challenges and share our lessons learned in deployment, maintenance, and utilization of the system over the past two years. We also share several examples of how the system has been used in applications at CareerBuilder to deliver value to end customers.
On Interpretation of Network Embedding via Taxonomy Induction
Authors: Ninghao Liu, Xiao Huang, Jundong Li, Xia Hu
Venue: KDD
Year: 2018

Network embedding has been increasingly used in many network analytics applications to generate low-dimensional vector representations, so that many off-the-shelf models can be applied to solve a wide variety of data mining tasks. However, similar to many other machine learning methods, network embedding results remain hard to be understood by users. Each dimension in the embedding space usually does not have any specific meaning, thus it is difficult to comprehend how the embedding instances are distributed in the reconstructed space. In addition, heterogeneous content information may be incorporated into network embedding, so it is challenging to specify which source of information is effective in generating the embedding results. In this paper, we investigate the interpretation of network embedding, aiming to understand how instances are distributed in embedding space, as well as explore the factors that lead to the embedding results. We resort to the post-hoc interpretation scheme, so that our approach can be applied to different types of embedding methods. Specifically, the interpretation of network embedding is presented in the form of a taxonomy. Effective objectives and corresponding algorithms are developed towards building the taxonomy. We also design several metrics to evaluate interpretation results. Experiments on real-world datasets from different domains demonstrate that, by comparing with the state-of-the-art alternatives, our approach produces effective and meaningful interpretation to embedding results.
Content to Node: Self-Translation Network Embedding
Authors: Jie Liu, Zhicheng He, Lai Wei, Yalou Huang
Venue: KDD
Year: 2018

This paper concerns the problem of network embedding (NE), whose aim is to learn low-dimensional representations for nodes in networks. Such dense vector representations offer great promises for many network analysis problems. However, existing NE approaches are still faced with challenges posed by the characteristics of complex networks in real-world applications. First, for many real-world networks associated with rich content information, previous NE methods tend to learn separated content and structure representations for each node, which requires a post-processing of combination. The empirical and simple combination strategies often make the final vector suboptimal. Second, the existing NE methods preserve the structure information by considering short and fixed neighborhood scope, such as the first- and/or the second-order proximities. However, it is hard to decide the scope of the neighborhood when facing a complex problem. To this end, we propose a novel sequence-to-sequence model based NE framework which is referred to as Self-Translation Network Embedding (STNE) model. With the sequences generated by random walks on a network, STNE learns the mapping that translates each sequence itself from the content sequence to the node sequence. On the one hand, the bi-directional LSTM encoder of STNE fuses the content and structure information seamlessly from the raw input. On the other hand, high-order proximity can be flexibly learned with the memories of LSTM to capture long-range structural information. By such self-translation from content to node, the learned hidden representations can be adopted as node embeddings. Extensive experimental results based on three real-world datasets demonstrate that the proposed STNE outperforms the state-of-the-art NE approaches. To facilitate reproduction and further study, we provide Internet access to the code and datasets\footnotehttp://dm.nankai.edu.cn/code/STNE.rar.