OpenRaiser/Intern-Atlas huggingface dataset:
Minimizing Legal Exposure of High-Tech Companies through Collaborative Filtering Methods
Authors: Bo Jin, Chao Che, Kuifei Yu, Yue Qu, Li Guo, Cuili Yao, Ruiyun Yu, Qiang Zhang
Venue: KDD
Year: 2016

Patent litigation not only covers legal and technical issues, it is also a key consideration for managers of high-technology (high-tech) companies when making strategic decisions. Patent litigation influences the market value of high-tech companies. However, this raises unique challenges. To this end, in this paper, we develop a novel recommendation framework to solve the problem of litigation risk prediction. We will introduce a specific type of patent-related litigation, that is, Section 337 investigations, which prohibit all acts of unfair competition, or any unfair trade practices, when exporting products to the United States. To build this recommendation framework, we collect and exploit a large amount of published information related to almost all Section 337 investigation cases. This study has two aims: (1) to predict the litigation risk in a specific industry category for high-tech companies and (2) to predict the litigation risk from competitors for high-tech companies. These aims can be achieved by mining historical investigation cases and related patents. Specifically, we propose two methods to meet the needs of both aims: a proximal slope one predictor and a time-aware predictor. Several factors are considered in the proposed methods, including the litigation risk if a company wants to enter a new market and the risk that a potential competitor would file a lawsuit against the new entrant. Comparative experiments using real-world data demonstrate that the proposed methods outperform several baselines with a significant margin.
Ranking Universities Based on Career Outcomes of Graduates
Authors: Navneet Kapur, Nikita I. Lytkin, Bee-Chung Chen, Deepak Agarwal, Igor Perisic
Venue: KDD
Year: 2016

Every year, millions of new students enter higher educational programs. Publicly available rankings of academic programs play a key role in prospective students' decisions regarding which universities to apply to and enroll in. While surveys indicate that majority of freshmen enter college to get good jobs after graduation, established methodologies for ranking universities rely on indirect indicators of career outcomes such as reputational assessments of the universities among academic peers, acceptance and graduation rates, learning environment, and availability of research funding. In addition, many of these methodologies rely on arbitrary choices of weighting factors for the different ranking indicators, and suffer from lack of analyses of statistical stability. In this paper, we addresses these challenges holistically by developing a novel methodology for ranking and recommending universities for different professions on the basis of career outcomes of professionals who graduated from those schools. Our methodology incorporates a number of techniques for achieving statistical stability, and represents a step towards personalized educational recommendations based on interests and ambitions of individuals. We have applied this methodology on LinkedIn's Economic Graph data of over 400 million professional from around the world. The resulting university rankings have been made available to the public and demonstrate that there are valuable insights to be gleaned from professional career data on LinkedIn.
Optimally Discriminative Choice Sets in Discrete Choice Models: Application to Data-Driven Test Design
Authors: Igor Labutov, Frans Schalekamp, Kelvin Luu, Hod Lipson, Christoph Studer
Venue: KDD
Year: 2016

Difficult multiple-choice (MC) questions can be made easy by providing a set of answer options of which most are obviously wrong. In the education literature, a plethora of instructional guides exist for crafting a suitable set of wrong choices (distractors) that enable the assessment of the students' understanding. The art of MC question design thus hinges on the question-maker's experience and knowledge of the potential misconceptions. In contrast, we advocate a data-driven approach, where correct and incorrect options are assembled directly from the students' own past submissions. Large-scale online classroom settings, such as massively open online courses (MOOCs), provide an opportunity to design optimal and adaptive multiple-choice questions that are maximally informative about the students' level of understanding of the material. In this work, we (i) develop a multinomial-logit discrete choice model for the setting of MC testing, (ii) derive an optimization objective for selecting optimally discriminative option sets, (iii) propose an algorithm for finding a globally-optimal solution, and (iv) demonstrate the effectiveness of our approach via synthetic experiments and a user study. We finally showcase an application of our approach to crowd-sourcing tests from technical online forums.
Profiling Users from Online Social Behaviors with Applications for Tencent Social Ads
Authors: Ching Law
Venue: KDD
Year: 2016

QQ and Wechat are the two largest instant messaging & social networks in China. Tencent Social Ads is the advertising platform for both Wechat and QQ, serving over 10B page views per day for several hundred million users. We strive to understand as much as possible on our users' characteristics, so as to serve the best personalized ads for them. The rich user behaviors on Tencent's diverse products lay a foundation in our user profiles on many dimensions, including demographics, interests, intents, transactions, physical locations, and devices, etc. In this talk, we will share our experience in large scale user data mining based on online social activities. We will discuss the challenges we face and the solutions we have devised so far. Some demographics data are obtained from user input, and thus would have gaps in both accuracy and coverage. We discuss the techniques in calibrating and verifying these data. We infer user interests from their social behaviors. For example, most QQ groups are not labelled properly, but by applying a large-scale topic model on the QQ memberships, we can effectively classify most QQ groups into an interest taxonomy. We also infer user interests from user's physical location check-ins and uploaded photos. User data can be collected from many diverse sources, including behaviors in various Tencent products, click and conversion in ad platform, and even seed customers collected by advertisers. We'll discuss the systems to merge these diverse data to provide a coherent view for our advertisers. High quality user labels are usually sparse. We implemented an algorithm for advertisers to reach more potential customers through user similarity computation based on user features as well as social graph inferences. We'll describe the system's contributions to ads quality. Top advertisers demand rich audience targeting solutions in combination of their own customer data, Tencent data, and possibly 3rd-party data. We'll discuss the data exchange platform that can facilitate collaborative applications with 3rd-party DSPs and DMPs.
Lightweight Monitoring of Distributed Streams
Authors: Arnon Lazerson, Daniel Keren, Assaf Schuster
Venue: KDD
Year: 2016

As data becomes dynamic, large, and distributed, there is increasing demand for what have become known as distributed stream algorithms. Since continuously collecting the data to a central server and processing it there incurs very high communication and computation complexities, it is advantageous to define local conditions at the nodes, such that -- as long as they are maintained -- some desirable global condition holds.
Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts
Authors: Benjamin Letham, Lydia M. Letham, Cynthia Rudin
Venue: KDD
Year: 2016

When an item goes out of stock, sales transaction data no longer reflect the original customer demand, since some customers leave with no purchase while others substitute alternative products for the one that was out of stock. Here we develop a Bayesian hierarchical model for inferring the underlying customer arrival rate and choice model from sales transaction data and the corresponding stock levels. The model uses a nonhomogeneous Poisson process to allow the arrival rate to vary throughout the day, and allows for a variety of choice models. Model parameters are inferred using a stochastic gradient MCMC algorithm that can scale to large transaction databases. We fit the model to data from a local bakery and show that it is able to make accurate out-of-sample predictions, and to provide actionable insight into lost cookie sales.
QUINT: On Query-Specific Optimal Networks
Authors: Liangyue Li, Yuan Yao, Jie Tang, Wei Fan, Hanghang Tong
Venue: KDD
Year: 2016

Measuring node proximity on large scale networks is a fundamental building block in many application domains, ranging from computer vision, e-commerce, social networks, software engineering, disaster management to biology and epidemiology. The state of the art (e.g., random walk based methods) typically assumes the input network is given a priori, with the known network topology and the associated edge weights. A few recent works aim to further infer the optimal edge weights based on the side information. This paper generalizes the challenge in multiple dimensions, aiming to learn optimal networks for node proximity measures. First (optimization scope), our proposed formulation explores a much larger parameter space, so that it is able to simultaneously infer the optimal network topology and the associated edge weights. This is important as a noisy or missing edge could greatly mislead the network node proximity measures. Second (optimization granularity), while all the existing works assume one common optimal network, be it given as the input or learned by the algorithms, exists for all queries, our method performs optimization at a much finer granularity, essentially being able to infer an optimal network that is specific to a given query. Third (optimization efficiency), we carefully design our algorithms with a linear complexity wrt the neighborhood size of the user preference set. We perform extensive empirical evaluations on a diverse set of 10+ real networks, which show that the proposed algorithms (1) consistently outperform the existing methods on all six commonly used metrics; (2) empirically scale sub-linearly to billion-scale networks and (3) respond in a fraction of a second.
A Real Linear and Parallel Multiple Longest Common Subsequences (MLCS) Algorithm
Authors: Yanni Li, Hui Li, Tihua Duan, Sheng Wang, Zhi Wang, Yang Cheng
Venue: KDD
Year: 2016

Information in various applications is often expressed as character sequences over a finite alphabet (e.g., DNA or protein sequences). In Big Data era, the lengths and sizes of these sequences are growing explosively, leading to grand challenges for the classical NP-hard problem, namely searching for the Multiple Longest Common Subsequences (MLCS) from multiple sequences. In this paper, we first unveil the fact that the state-of-the-art MLCS algorithms are unable to be applied to long and large-scale sequences alignments. To overcome their defects and tackle the longer and large-scale or even big sequences alignments, based on the proposed novel problem-solving model and various strategies, e.g., parallel topological sorting, optimal calculating, reuse of intermediate results, subsection calculation and serialization, etc., we present a novel parallel MLCS algorithm. Exhaustive experiments on the datasets of both synthetic and real-world biological sequences demonstrate that both the time and space of the proposed algorithm are only linear in the number of dominants from aligned sequences, and the proposed algorithm significantly outperforms the state-of-the-art MLCS algorithms, being applicable to longer and large-scale sequences alignments.
Scalable Fast Rank-1 Dictionary Learning for fMRI Big Data Analysis
Authors: Xiang Li, Milad Makkie, Binbin Lin, Mojtaba Sedigh Fazli, Ian Davidson, Jieping Ye, Tianming Liu, Shannon Quinn
Venue: KDD
Year: 2016

It has been shown from various functional neuroimaging studies that sparsity-regularized dictionary learning could achieve superior performance in decomposing comprehensive and neuroscientifically meaningful functional networks from massive fMRI signals. However, the computational cost for solving the dictionary learning problem has been known to be very demanding, especially when dealing with large-scale data sets. Thus in this work, we propose a novel distributed rank-1 dictionary learning (D-r1DL) model and apply it for fMRI big data analysis. The model estimates one rank-1 basis vector with sparsity constraint on its loading coefficient from the input data at each learning step through alternating least squares updates. By iteratively learning the rank-1 basis and deflating the input data at each step, the model is then capable of decomposing the whole set of functional networks. We implement and parallelize the rank-1 dictionary learning algorithm using Spark engine and deployed the resilient distributed dataset (RDDs) abstracts for the data distribution and operations. Experimental results from applying the model on the Human Connectome Project (HCP) data show that the proposed D-r1DL model is efficient and scalable towards fMRI big data analytics, thus enabling data-driven neuroscientific discovery from massive fMRI big data in the future.
Multi-Task Feature Interaction Learning
Authors: Kaixiang Lin, Jianpeng Xu, Inci M. Baytas, Shuiwang Ji, Jiayu Zhou
Venue: KDD
Year: 2016

One major limitation of linear models is the lack of capability to capture predictive information from interactions between features. While introducing high-order feature interaction terms can overcome this limitation, this approach tremendously increases the model complexity and imposes significant challenges in the learning against overfitting. In this paper, we proposed a novel Multi-Task feature Interaction Learning~(MTIL) framework to exploit the task relatedness from high-order feature interactions, which provides better generalization performance by inductive transfer among tasks via shared representations of feature interactions. We formulate two concrete approaches under this framework and provide efficient algorithms: the shared interaction approach and the embedded interaction approach. The former assumes tasks share the same set of interactions, and the latter assumes feature interactions from multiple tasks come from a shared subspace. We have provided efficient algorithms for solving the two approaches. Extensive empirical studies on both synthetic and real datasets have demonstrated the effectiveness of the proposed framework.
Optimal Reserve Prices in Upstream Auctions: Empirical Application on Online Video Advertising
Authors: Miguel Angel Alcobendas Lisbona, Sheide Chammas, Kuang-chih Lee
Venue: KDD
Year: 2016

We consider optimal reserve prices in BrightRoll Video Exchange when the inventory opportunity comes from other exchanges (downstream marketplaces). We show that the existence of downstream auctions impacts the optimal floor. Moreover, it renders the classical derivation of the floor set by a monopolist inadequate and suboptimal. We derive the new downstream-corrected reserve price and compare its performance with respect to existing floors and the classical optimal monopoly price. In our application, the downstream-corrected reserve price proves superior to both. The proposed model also deals with data challenges commonly faced by exchanges: limited number of logged bids in an auction, and uncertainty regarding the bidding behavior in other exchanges.
CompanyDepot: Employer Name Normalization in the Online Recruitment Industry
Authors: Qiaoling Liu, Faizan Javed, Matt McNair
Venue: KDD
Year: 2016

Entity linking links entity mentions in text to the corresponding entities in a knowledge base (KB) and has many applications in both open domain and specific domains. For example, in the recruitment domain, linking employer names in job postings or resumes to entities in an employer KB is very important to many business applications. In this paper, we focus on this employer name normalization task, which has several unique challenges: handling employer names from both job postings and resumes, leveraging the corresponding location context, and handling name variations, irrelevant input data, and noises in the KB. We present a system called CompanyDepot which contains a machine learning based approach CompanyDepot-ML and a heuristic approach CompanyDepot-H to address these challenges in three steps: (1) searching for candidate entities based on a customized search engine for the KB; (2) ranking the candidate entities using learning-to-rank methods or heuristics; and (3) validating the top-ranked entity via binary classification or heuristics. While CompanyDepot-ML shows better extendability and flexibility, CompanyDepot-H serves as a strong baseline and useful way to collect training data for CompanyDepot-ML. The proposed system achieves 2.5%-21.4% higher coverage at the same precision level compared to an existing system used at CareerBuilder over multiple real-world datasets. Applying the system to a similar task of academic institution name normalization further shows the generalization ability of the method.
Audience Expansion for Online Social Network Advertising
Authors: Haishan Liu, David Pardoe, Kun Liu, Manoj Thakur, Frank Cao, Chongzhe Li
Venue: KDD
Year: 2016

Online social network advertising platforms, such as that provided by LinkedIn, generally allow marketers to specify targeting options so that their ads appear to a desired demographic. Audience Expansion is a technique developed at LinkedIn to simplify targeting and identify new audiences with similar attributes to the original target audience. We developed two methods to achieve Audience Expansion: campaign-agnostic expansion and campaign-aware expansion. In this paper, we describe the details of these methods, present in-depth analysis of their trade-offs, and demonstrate a hybrid strategy that possesses the combined strength of both methods. Through large scale online experiments, we show the effectiveness of the proposed approach, and as a result, the benefits it brings to the whole marketplace including both LinkedIn and advertisers. The achieved benefits can be characterized as: 1) simplified targeting process and increased reach for advertisers, and 2) better utilization of LinkedIn's ads inventory and higher and more efficient market participation.
Infinite Ensemble for Image Clustering
Authors: Hongfu Liu, Ming Shao, Sheng Li, Yun Fu
Venue: KDD
Year: 2016

Image clustering has been a critical preprocessing step for vision tasks, e.g., visual concept discovery, content-based image retrieval. Conventional image clustering methods use handcraft visual descriptors as basic features via K-means, or build the graph within spectral clustering. Recently, representation learning with deep structure shows appealing performance in unsupervised feature pre-treatment. However, few studies have discussed how to deploy deep representation learning to image clustering problems, especially the unified framework which integrates both representation learning and ensemble clustering for efficient image clustering still remains void. In addition, even though it is widely recognized that with the increasing number of basic partitions, ensemble clustering gets better performance and lower variances, the best number of basic partitions for a given data set is a pending problem. In light of this, we propose the Infinite Ensemble Clustering (IEC), which incorporates the power of deep representation and ensemble clustering in a one-step framework to fuse infinite basic partitions. Generally speaking, a set of basic partitions is firstly generated from the image data, then by converting the basic partitions to the 1-of-K codings, we link the marginalized auto-encoder to the infinite ensemble clustering with i.i.d. basic partitions, which can be approached by the closed-form solutions, finally we follow the layer-wise training procedure and feed the concatenated deep features to K-means for final clustering. Extensive experiments on diverse vision data sets with different levels of visual descriptors demonstrate both the time efficiency and superior performance of IEC compared to the state-of-the-art ensemble clustering and deep clustering methods.
From Online Behaviors to Offline Retailing
Authors: Ping Luo, Su Yan, Zhiqiang Liu, Zhiyong Shen, Shengwen Yang, Qing He
Venue: KDD
Year: 2016

To combat the ease of online shopping in pajamas, offline mall owners focus increasingly on driving satisfaction and improving retention by identifying customers' preferences. However, most of these studies are based on customers' offline consuming history only. Benefiting from the internet, we can also get customers' online behaviors, such as the search logs, web browsing logs, online shopping logs, and so on. Might these seemingly irrelevant information from two different modalities (i.e. online and offline) be somehow interrelated? How can we make use of the online behaviors and offline actions jointly to promote recommendation for offline retailing? In this study, we formulate this task as a cross-modality recommendation problem, and present its solution via a proposed probabilistic graphical model, called Online-to-Offline Topic Modeling (O2OTM). Specifically, this method explicitly models the relationships between online and offline topics so that the likelihood of both online and offline behaviors is maximized. Then, the recommendation is made only based on the pairs of online and offline topics, denoted by (t,l), with high values of lift, such that the existence of the online topic $t$ greatly increases the response on the corresponding offline topic $l$ compared with the average response for the population without the online topic t. Furthermore, we evaluate this solution in both live and retrospect experiments. The real-world deployment of this model for the anniversary promotion campaign of a famous shopping mall in Beijing shows that our approach increases the occurred customer purchases per promotion message by 29.75\% compared with the baseline. Also, our model finds some interesting interpretable relationships between the online search topics and offline brand topics.
Images Don't Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank
Authors: Corey Lynch, Kamelia Aryafar, Josh Attenberg
Venue: KDD
Year: 2016

Search is at the heart of modern e-commerce. As a result, the task of ranking search results automatically (learning to rank) is a multibillion dollar machine learning problem. Traditional models optimize over a few hand-constructed features based on the item's text. In this paper, we introduce a multimodal learning to rank model that combines these traditional features with visual semantic features transferred from a deep convolutional neural network. In a large scale experiment using data from the online marketplace Etsy, we verify that moving to a multimodal representation significantly improves ranking quality. We show how image features can capture fine-grained style information not available in a text-only representation. In addition, we show concrete examples of how image information can successfully disentangle pairs of highly different items that are ranked similarly by a text-only model.