OpenRaiser/Intern-Atlas huggingface dataset:
Inspiration or Preparation?: Explaining Creativity in Scientific Enterprise
Authors: Xinyang Zhang, Dashun Wang, Ting Wang
Venue: CIKM
Year: 2016

Human creativity is the ultimate driving force behind scientific progress. While the building blocks of innovations are often embodied in existing knowledge, it is creativity that blends seemingly disparate ideas. Existing studies have made striding advances in quantifying creativity of scientific publications by investigating their citation relationships. Yet, little is known hitherto about the underlying mechanisms governing scientific creative processes, largely due to that a paper's references, at best, only partially reflect its authors' actual information consumption. This work represents an initial step towards fine-grained understanding of creative processes in scientific enterprise. In specific, using two web-scale longitudinal datasets (120.1 million papers and 53.5 billion web requests spanning 4 years), we directly contrast authors' information consumption behaviors against their knowledge products. We find that, of 59.0% papers across all scientific fields, 25.7% of their creativity can be readily explained by information consumed by their authors. Further, by leveraging these findings, we develop a predictive framework that accurately identifies the most critical knowledge to fostering target scientific innovations. We believe that our framework is of fundamental importance to the study of scientific creativity. It promotes strategies to stimulate and potentially automate creative processes, and provides insights towards more effective designs of information recommendation platforms.
Information Diffusion at Workplace
Authors: Jiawei Zhang, Philip S. Yu, Yuanhua Lv, Qianyi Zhan
Venue: CIKM
Year: 2016

People nowadays need to spend a large amount of time on their work everyday and workplace has become an important social occasion for effective communication and information exchange among employees. Besides traditional online contacts (e.g., face-to-face meetings and telephone calls), to facilitate the communication and cooperation among employees, a new type of online social networks has been launched inside the firewalls of many companies, which are named as the "enterprise social networks" (ESNs). In this paper, we want to study the information diffusion among employees at workplace via both online ESNs and online contacts. This is formally defined as the IDE (Information Diffusion in Enterprise) problem. Several challenges need to be addressed in solving the IDE problem: (1) diffusion channel extraction from online ESN and online contacts; (2) effective aggregation of the information delivered via different diffusion channels; and (3) communication channel weighting and selection. A novel information diffusion model, Muse (Multi-source Multi-channel Multi-topic diffUsion SElection), is introduced in this paper to resolve these challenges. Extensive experiments conducted on real-world ESN and organizational chart dataset demonstrate the outstanding performance of Muse in addressing the IDE problem.
Collective Classification via Discriminative Matrix Factorization on Sparsely Labeled Networks
Authors: Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang
Venue: CIKM
Year: 2016

We address the problem of classifying sparsely labeled networks, where labeled nodes in the network are extremely scarce. Existing algorithms, such as collective classification, have been shown to be effective for jointly deriving labels of related nodes, by exploiting class label dependencies among neighboring nodes. However, when the underlying network is sparsely labeled, most nodes have too few or even no connections to labeled nodes. This makes it very difficult to leverage supervised knowledge from labeled nodes to accurately estimate label dependencies, thereby largely degrading the classification accuracy. In this paper, we propose a novel discriminative matrix factorization (DMF) based algorithm that effectively learns a latent network representation by exploiting topological paths between labeled and unlabeled nodes, in addition to nodes' content information. The main idea is to use matrix factorization to obtain a compact representation of the network that fully encodes nodes' content information and network structure, and unleash discriminative power inferred from labeled nodes to directly benefit collective classification. To achieve this, we formulate a new matrix factorization objective function that integrates network representation learning with an empirical loss minimization for classifying node labels. An efficient optimization algorithm based on conjugate gradient methods is proposed to solve the new objective function. Experimental results on real-world networks show that DMF yields superior performance gain over the state-of-the-art baselines on sparsely labeled networks.
LDA Revisited: Entropy, Prior and Convergence
Authors: Jianwei Zhang, Jia Zeng, Mingxuan Yuan, Weixiong Rao, Jianfeng Yan
Venue: CIKM
Year: 2016

Inference algorithms of latent Dirichlet allocation (LDA), either for small or big data, can be broadly categorized into expectation-maximization (EM), variational Bayes (VB) and collapsed Gibbs sampling (GS). Looking for a unified understanding of these different inference algorithms is currently an important open problem. In this paper, we revisit these three algorithms from the entropy perspective, and show that EM can achieve the best predictive perplexity (a standard performance metric for LDA accuracy) by minimizing directly the cross entropy between the observed word distribution and LDA's predictive distribution. Moreover, EM can change the entropy of LDA's predictive distribution through tuning priors of LDA, such as the Dirichlet hyperparameters and the number of topics, to minimize the cross entropy with the observed word distribution. Finally, we propose the adaptive EM (AEM) algorithm that converges faster and more accurate than the current state-of-the-art SparseLDA [20] and AliasLDA [12] from small to big data and LDA models. The core idea is that the number of active topics, measured by the residuals between E-steps at successive iterations, decreases significantly, leading to the amortized σ(1) time complexity in terms of the number of topics. The open source code of AEM is available at GitHub.
Constructing Reliable Gradient Exploration for Online Learning to Rank
Authors: Tong Zhao, Irwin King
Venue: CIKM
Year: 2016

With the rapid development of information retrieval (IR) systems, online learning to rank (OLR) approaches, which allow retrieval systems to automatically learn best parameters from user interactions, have attracted great research interests in recent years. In OLR, the algorithms usually need to explore some uncertain retrieval results for updating current parameters meanwhile guaranteeing to produce quality retrieval results by exploiting what have already been learned, and the final retrieval results is an interleaved list from both exploratory and exploitative results. However, existing OLR algorithms perform exploration based on either only one stochastic direction or multiple randomly selected stochastic directions, which always involve large variance and uncertainty into the exploration, and may further harm the retrieval quality. Moreover, little historical exploration knowledge is considered when conducting current exploration.
Regularizing Structured Classifier with Conditional Probabilistic Constraints for Semi-supervised Learning
Authors: Vincent Wenchen Zheng, Kevin Chen-Chuan Chang
Venue: CIKM
Year: 2016

Constraints have been shown as an effective way to incorporate unlabeled data for semi-supervised structured classification. We recognize that, constraints are often conditional and probabilistic; moreover, a constraint can have its condition depend on either just observations (which we call x-type constraint) or even hidden variables (which we call y-type constraint). We wish to design a constraint formulation that can flexibly model the constraint probability for both x-type and y-type constraints, and later use it to regularize general structured classifiers for semi-supervision. Surprisingly, none of the existing models have such a constraint formulation. Thus in this paper, we propose a new conditional probabilistic formulation for modeling both x-type and y-type constraints. We also recognize the inference complication for y-type constraint, and propose a systematic selective evaluation approach to efficiently realize the constraints. Finally, we evaluate our model in three applications, including named entity recognition, part-of-speech tagging and entity information extraction, with totally nine data sets. We show that our model is generally more accurate and efficient than the state-of-the-art baselines. Our code and data are available at https://bitbucket.org/vwz/cikm2016-cpf/.
Graph-Based Multi-Modality Learning for Clinical Decision Support
Authors: Ziwei Zheng, Xiaojun Wan
Venue: CIKM
Year: 2016

The task of clinical decision support (CDS) involves retrieval and ranking of medical journal articles for medical records of diagnosis, test or treatment. Previous studies on this task are based on bag-of-words representations of document texts and general retrieval models. In this paper, we propose to use the paragraph vector technique to learn the latent semantic representation of texts and treat the latent semantic representations and the original bag-of-words representations as two different modalities. We then propose to use the graph-based multi-modality learning algorithm for document re-ranking. Experimental results on two TREC-CDS benchmark datasets demonstrate the excellent performance of our proposed approach.
Urban Traffic Prediction through the Second Use of Inexpensive Big Data from Buildings
Authors: Zimu Zheng, Dan Wang, Jian Pei, Yi Yuan, Cheng Fan, Linda Fu Xiao
Venue: CIKM
Year: 2016

Traffic prediction, particularly in urban regions, is an important application of tremendous practical value. In this paper, we report a novel and interesting case study of urban traffic prediction in Central, Hong Kong, one of the densest urban areas in the world. The novelty of our study is that we make good second use of inexpensive big data collected from the Hong Kong International Commerce Centre (ICC), a 118-story building in Hong Kong where more than 10,000 people work. As building environment data are much cheaper to obtain than traffic data, we demonstrate that it is highly effective to estimate building occupancy information using building environment data, and then to further use the information on occupancy to provide traffic predictions in the proximate area. Scientifically, we investigate how and to what extent building data can complement traffic data in predicting traffic. In general, this study sheds new light on the development of accurate data mining applications through the second use of inexpensive big data.
Ensemble of Anchor Adapters for Transfer Learning
Authors: Fuzhen Zhuang, Ping Luo, Sinno Jialin Pan, Hui Xiong, Qing He
Venue: CIKM
Year: 2016

In the past decade, there have been a large number of transfer learning algorithms proposed for various real-world applications. However, most of them are vulnerable to negative transfer since their performance is even worse than traditional supervised models. Aiming at more robust transfer learning models, we propose an ENsemble framework of anCHOR adapters (ENCHOR for short), in which an anchor adapter adapts the features of instances based on their similarities to a specific anchor (i.e., a selected instance). Specifically, the more similar to the anchor instance, the higher degree of the original feature of an instance remains unchanged in the adapted representation, and vice versa. This adapted representation for the data actually expresses the local structure around the corresponding anchor, and then any transfer learning method can be applied to this adapted representation for a prediction model, which focuses more on the neighborhood of the anchor. Next, based on multiple anchors, multiple anchor adapters can be built and combined into an ensemble for final output. Additionally, we develop an effective measure to select the anchors for ensemble building to achieve further performance improvement. Extensive experiments on hundreds of text classification tasks are conducted to demonstrate the effectiveness of ENCHOR. The results show that: when traditional supervised models perform poorly, ENCHOR (based on only 8 selected anchors) achieves $6%-13%$ increase in terms of average accuracy compared with the state-of-the-art methods, and it greatly alleviates negative transfer.
PRO: Preference-Aware Recurring Query Optimization
Authors: Zhongfang Zhuang, Chuan Lei, Elke A. Rundensteiner, Mohamed Y. Eltabakh
Venue: CIKM
Year: 2016

While recurring queries over evolving data are the bedrock of the analytical applications, resources demanded to process a large amount of data for each recurring execution can be a fatal bottleneck in cost-sensitive cloud computing environments. It is thus imperative to design a system responsive to users' preferences regarding how resources should be utilized. In this work, we propose PRO, a preference-aware recurring query processing system that optimizes recurring query executions complying with user preferences. First, we show that finding an optimal is an NP-complete problem due to the cost interdependencies between consecutive executions. We propose an execution relation graph (ERG) model that effectively incorporates these dependencies between executions. This model enables us to transform our problem into a well-known graph problem. We then design a graph-based approach (called PRO-OPT) leveraging dynamic programming and pruning techniques with pseudo-polynomial complexity. Our experiments confirm that PRO consistently outperforms state-of-the-art solutions by 9 fold in processing time under a rich variety of circumstances on the Wikipedia datasets.
Query Variations and their Effect on Comparing Information Retrieval Systems
Authors: Guido Zuccon, João R. M. Palotti, Allan Hanbury
Venue: CIKM
Year: 2016

We explore the implications of using query variations for evaluating information retrieval systems and how these variations should be exploited to compare system effectiveness. Current evaluation approaches consider the availability of a set of topics (information needs), and only one expression of each topic in the form of a query is used for evaluation and system comparison. While there is strong evidence that considering query variations better models the usage of retrieval systems and accounts for the important user aspect of user variability, it is unclear how to best exploit query variations for evaluating and comparing information retrieval systems.
Metacrate: Organize and Analyze Millions of Data Profiles
Authors: Sebastian Kruse, David Hahn, Marius Walter, Felix Naumann
Venue: CIKM
Year: 2017

Databases are one of the great success stories in IT. However, they have been continuously increasing in complexity, hampering operation, maintenance, and upgrades. To face this complexity, sophisticated methods for schema summarization, data cleaning, information integration, and many more have been devised that usually rely on data profiles, such as data statistics, signatures, and integrity constraints. Such data profiles are often extracted by automatic algorithms, which entails various problems: The profiles can be unfiltered and huge in volume; different profile types require different complex data structures; and the various profile types are not integrated with each other. We introduce Metacrate, a system to store, organize, and analyze data profiles of relational databases, thereby following the proven design of databases. In particular, we (i) propose a logical and a physical data model to store all kinds of data profiles in a scalable fashion; (ii) describe an analytics layer to query, integrate, and analyze the profiles efficiently; and (iii) implement on top a library of established algorithms to serve use cases, such as schema discovery, database refactoring, and data cleaning.
Learning Edge Representations via Low-Rank Asymmetric Projections
Authors: Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou
Venue: CIKM
Year: 2017

We propose a new method for embedding graphs while preserving directed edge information. Learning such continuous-space vector representations (or embeddings) of nodes in a graph is an important first step for using network information (from social networks, user-item graphs, knowledge bases, etc.) in many machine learning tasks. Unlike previous work, we (1) explicitly model an edge as a function of node embeddings, and we (2) propose a novel objective, the graph likelihood, which contrasts information from sampled random walks with non-existent edges. Individually, both of these contributions improve the learned representations, especially when there are memory constraints on the total size of the embeddings. When combined, our contributions enable us to significantly improve the state-of-the-art by learning more concise representations that better preserve the graph structure. We evaluate our method on a variety of link-prediction task including social networks, collaboration networks, and protein interactions, showing that our proposed method learn representations with error reductions of up to 76% and 55%, on directed and undirected graphs. In addition, we show that the representations learned by our method are quite space efficient, producing embeddings which have higher structure-preserving accuracy but are 10 times smaller.
PODIUM: Procuring Opinions from Diverse Users in a Multi-Dimensional World
Authors: Yael Amsterdamer, Oded Goldreich
Venue: CIKM
Year: 2017

The procurement of opinions is an important task in many contexts. When selecting members of a certain population to ask for their opinions, diversity inside the selected subset is a central consideration. People with diverse profiles are assumed to provide a wider range of opinions and thus to better represent the opinions of the entire population. However, in platforms with a large user base such as crowdsourcing applications and social networks, defining and realizing notions of diversity are both nontrivial. The profiles of users typically contain information that is high-dimensional and semantically rich. We present PODIUM, a tool for opinion procurement that accounts for complex user profiles and enables customizable user selection. Beyond selecting a subset of users with diverse profiles, PODIUM produces explanation for the choice of each user and visual aids to compare the selected subset to the entire population on different dimensions. We demonstrate the use of PODIUM on the TripAdvisor user base, which further enables us to examine the ability of our system to predict diverse opinions in user reviews.
Unsupervised Feature Selection with Joint Clustering Analysis
Authors: Shuai An, Jun Wang, Jinmao Wei, Zhenglu Yang
Venue: CIKM
Year: 2017

Unsupervised feature selection has raised considerable interests in the past decade, due to its remarkable performance in reducing dimensionality without any prior class information. Preserving reliable locality information and achieving excellent cluster separation are two critical issues for unsupervised feature selection. However, existing methods cannot tackle two issues simultaneously. To address the problems, we propose a novel unsupervised approach that integrates sparse feature selection and robust joint clustering analysis. The joint clustering analysis seamlessly unifies the spectral clustering and the orthogonal basis clustering. Specifically, a probabilistic neighborhood graph is utilized to preserve reliable locality information in the spectral clustering, and an orthogonal basis matrix is incorporated to achieve excellent cluster separation in the orthogonal basis clustering. A compact and effective iterative algorithm is designed to optimize the proposed selection framework. Extensive experiments on both synthetic data and real-world data validate the effectiveness of our approach under various evaluation indices.
Citation Metadata Extraction via Deep Neural Network-based Segment Sequence Labeling
Authors: Dong An, Liangcai Gao, Zhuoren Jiang, Runtao Liu, Zhi Tang
Venue: CIKM
Year: 2017

Citation metadata extraction plays an important role in academic information retrieval and knowledge management. Current works on this task generally use rule-based, template-based or learning-based approaches but these methods usually either rely on handcrafted features or are limited with domains. Recently, neural networks have shown strong ability in addressing sequence labeling tasks.
Building a Dossier on the Cheap: Integrating Distributed Personal Data Resources Under Cost Constraints
Authors: Imrul Chowdhury Anindya, Harichandan Roy, Murat Kantarcioglu, Bradley A. Malin
Venue: CIKM
Year: 2017

A wide variety of personal data is routinely collected by numerous organizations that, in turn, share and sell their collections for analytic investigations (e.g., market research). To preserve privacy, certain identifiers are often redacted, perturbed or even removed. A substantial number of attacks have shown that, if care is not taken, such data can be linked to external resources to determine the explicit identifiers (e.g., personal names) or infer sensitive attributes (e.g., income) for the individuals from whom the data was collected. As such, organizations increasingly rely upon record linkage methods to assess the risk such attacks pose and adopt countermeasures accordingly. Traditional linkage methods assume only two datasets would be linked (e.g., linking de-identified hospital discharge to identified voter registration lists), but with the advent of a multi-billion dollar data broker industry, modern adversaries have access to a massive data stash of multiple datasets that can be leveraged. Still, realistic adversaries have budget constraints that prevent them from obtaining and integrating all relevant datasets. Thus, in this work, we investigate a novel privacy risk assessment framework, based on adversaries who plan an integration of datasets for the most accurate estimate of targeted sensitive attributes under a certain budget. To solve this problem, we introduce a graph-based formulation of the problem and predictive modeling methods to prioritize data resources for linkage. We perform an empirical analysis using real world voter registration data from two different U.S. states and show that the methods can be used efficiently to accurately estimate potentially sensitive information disclosure risks even under a non-trivial amount of noise.
A Novel Approach for Efficient Computation of Community Aware Ridesharing Groups
Authors: Samiul Anwar, Shuha Nabila, Tanzima Hashem
Venue: CIKM
Year: 2017

The evolution of ridesharing services has reduced the road traffic congestions in recent years. However, a major concern for ridesharing services is sharing rides with strangers. To address this issue, a few ridesharing approaches have considered social closeness of group members for identifying a ridesharing group. Again, users do not feel comfortable to disclose such personal data (e.g, friendship information) with an untrusted service provider for privacy reasons. We propose a novel way to form ridesharing groups that reveals user social data in community levels, and ensures that a group member shares at least k common communities with at least other m members in the ridesharing group, where k and m are personalized parameters of every group member. We formulate a Community aware Ridesharing Group (CaRG) query that satisfies the constraints of m and k, and returns a ridesharing group with the minimum cost in terms of the spatial proximity of riders from the driver. We show in experiments that our approach to process CaRG queries outperforms a baseline approach with a large margin.