OpenRaiser/Intern-Atlas huggingface dataset:
Knowledge graph embedding with diversity of structures
Authors: Wen Zhang
Venue: Conference WWW 2017
Year: 2017

In recent years, different web knowledge graphs, both free and commercial, have been created. Knowledge graphs use relations between entities to describe facts in the world. We engage in embedding a large scale knowledge graph into a continuous vector space. TransE, TransH, TransR and TransD are promising methods proposed in recent years and achieved state-of-the-art predictive performance. In this paper, we discuss that graph structures should be considered in embedding and propose to embed substructures called "one-relation-circle" (ORC) to further improve the performance of the above methods as they are unable to encode ORC substructures. Some complex models are capable of handling ORC structures but sacrifice efficiency in the process. To make a good trade-off between the model capacity and efficiency, we propose a method to decompose ORC substructures by using two vectors to represent the entity as a head or tail entity with the same relation. In this way, we can encode the ORC structure properly when apply it to TransH, TransR and TransD with almost the same model complexity of themselves. We conduct experiments on link prediction with benchmark dataset WordNet. Our experiments show that applying our method improves the results compared with the corresponding original results of TransH, TransR and TransD.
Learning latent representations of nodes for classifying in heterogeneous social networks
Authors: Yann Jacob, Ludovic Denoyer, Patrick Gallinari
Venue: Conference WSDM 2014
Year: 2014

Social networks are heterogeneous systems composed of different types of nodes (e.g. users, content, groups, etc.) and relations (e.g. social or similarity relations). While learning and performing inference on homogeneous networks have motivated a large amount of research, few work exists on heterogeneous networks and there are open and challenging issues for existing methods that were previously developed for homogeneous networks. We address here the specific problem of nodes classification and tagging in heterogeneous social networks, where different types of nodes are considered, each type with its own label or tag set. We propose a new method for learning node representations onto a latent space, common to all the different node types. Inference is then performed in this latent space. In this framework, two nodes connected in the network will tend to share similar representations regardless of their types. This allows bypassing limitations of the methods based on direct extensions of homogenous frameworks and exploiting the dependencies and correlations between the different node types. The proposed method is tested on two representative datasets and compared to state-of-the-art methods and to baselines.
Node immunization over infectious period
Authors: Chonggang Song, Wynne Hsu, Mong-Li Lee
Venue: Conference CIKM 2015
Year: 2015

Locating nodes to immunize in computer/social networks to control the spread of virus or rumors has become an important problem. In real world contagions, nodes may get infected by external sources when the propagation is underway. While most studies formalize the problem in a setting where contagion starts at one time point, we model a more realistic situation where there are likely to be many breakouts of contagions over a time window. We call this the node immunization over infectious period (NIIP) problem. We show that the NIIP problem is NP-hard and remains so even in directed acyclic graphs. We propose a NIIP algorithm to select $k$ nodes to immunize over a time period. Simulation is performed to estimate a good distribution of $k$ over the time period. For each time point, the NIIP algorithm will make decisions which nodes to immunize given the estimated value of $k$ for that time point. Experiments show that the proposed NIIP algorithm outperform the state-of-the-art algorithms in terms of both effectiveness and efficiency.
Scalable vaccine distribution in large graphs given uncertain data
Authors: Yao Zhang, B. Aditya Prakash
Venue: Conference CIKM 2014
Year: 2014

Given an noisy or sampled snapshot of a network, like a contact-network or the blogosphere, in which an infection (or meme/virus) has been spreading for some time, what are the best nodes to immunize (vaccinate)? Manipulating graphs via node removal by itself is an important problem in multiple different domains like epidemiology, public health and social media. Moreover, it is important to account for uncertainty as typically surveillance data on who is infected is limited or the data is sampled. Efficient algorithms for such a problem can help public-health experts take more informed decisions.
What can quantum theory bring to information retrieval
Authors: Benjamin Piwowarski, Ingo Frommholz, Mounia Lalmas, Keith van Rijsbergen
Venue: Conference CIKM 2010
Year: 2010

The probabilistic formalism of quantum physics is said to provide a sound basis for building a principled information retrieval framework. Such a framework can be based on the notion of information need vector spaces where events, such as document relevance or observed user interactions, correspond to subspaces. As in quantum theory, a probability distribution over these subspaces is defined through weighted sets of state vectors (density operators), and used to represent the current view of the retrieval system on the user information need. Tensor spaces can be used to capture different aspects of information needs. Our evaluation shows that the framework can lead to acceptable performance in an ad-hoc retrieval task. Going beyond this, we discuss the potential of the framework for three active challenges in information retrieval, namely, interaction, novelty and diversity.
Noise-contrastive estimation for answer selection with deep neural networks
Authors: Jinfeng Rao, Hua He, Jimmy Lin
Venue: Conference CIKM 2016
Year: 2016

We study answer selection for question answering, in which given a question and a set of candidate answer sentences, the goal is to identify the subset that contains the answer. Unlike previous work which treats this task as a straightforward pointwise classification problem, we model this problem as a ranking task and propose a pairwise ranking approach that can directly exploit existing pointwise neural network models as base components. We extend the Noise-Contrastive Estimation approach with a triplet ranking loss function to exploit interactions in triplet inputs over the question paired with positive and negative examples. Experiments on TrecQA and WikiQA datasets show that our approach achieves state-of-the-art effectiveness without the need for external knowledge sources or feature engineering.
On-board Analysis of Uncalibrated Data for a Spacecraft at Mars
Authors: Rebecca Castaño, Kiri Wagstaff, Steve A. Chien, Timothy M. Stough, Benyang Tang
Venue: Conference KDD 2007
Year: 2007

Analyzing data on-board a spacecraft as it is collected enables several advanced spacecraft capabilities, such as prioritizing observations to make the best use of limited bandwidth and reacting to dynamic events as they happen. In this paper, we describe how we addressed the unique challenges associated with on-board mining of data as it is collected: uncalibrated data, noisy observations, and severe limitations on computational and memory resources. The goal of this effort, which falls into the emerging application area of spacecraft-based data mining, was to study three specific science phenomena on Mars. Following previous work that used a linear support vector machine (SVM) on-board the Earth Observing 1 (EO-1)spacecraft, we developed three data mining techniques for use on-board the Mars Odyssey spacecraft. These methods range from simple thresholding to state-of-the-art reduced-set SVM technology. We tested these algorithms on archived data in a flight software testbed. We also describe a significant, serendipitous science discovery of this data mining effort: the confirmation of a water ice annulus around the north polar cap of Mars. We conclude with a discussion on lessons learned in developing algorithms for use on-board a spacecraft.
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
Authors: Rong Pan, Martin Scholz
Venue: Conference KDD 2009
Year: 2009

One-Class Collaborative Filtering (OCCF) is a task that naturally emerges in recommender system settings. Typical characteristics include: Only positive examples can be observed, classes are highly imbalanced, and the vast majority of data points are missing. The idea of introducing weights for missing parts of a matrix has recently been shown to help in OCCF. While existing weighting approaches mitigate the first two problems above, a sparsity preserving solution that would allow to efficiently utilize data sets with e.g., hundred thousands of users and items has not yet been reported. In this paper, we study three different collaborative filtering frameworks: Low-rank matrix approximation, probabilistic latent semantic analysis, and maximum-margin matrix factorization. We propose two novel algorithms for large-scale OCCF that allow to weight the unknowns. Our experimental results demonstrate their effectiveness and efficiency on different problems, including the Netflix Prize data.
Efficient Similar Region Search with Deep Metric Learning
Authors: Yiding Liu, Kaiqi Zhao, Gao Cong
Venue: Conference KDD 2018
Year: 2018

With the proliferation of mobile devices and location-based services, rich geo-tagged data is becoming prevalent and this offer great opportunities to understand different geographical regions (e.g., shopping areas). However, the huge number of regions with complicated spatial information are expensive for people to explore and understand. To solve this issue, we study the problem of searching similar regions given a user specified query region. The problem is challenging in both similarity definition and search efficiency. To tackle the two challenges, we propose a novel solution equipped by (1) a deep learning approach to learning the similarity that considers both object attributes and the relative locations between objects; and (2) an efficient branch and bound search algorithm for finding top-N similar regions. Moreover, we propose an approximation method to further improve the efficiency by slightly sacrificing the accuracy. Our experiments on three real world datasets demonstrate that our solution improves both the accuracy and search efficiency by a significant margin compared with the state-of-the-art methods.
Shape discovery from unlabeled image collections
Authors: Yong Jae Lee, Kristen Grauman
Venue: Conference CVPR 2009
Year: 2009

Can we discover common object shapes within unlabeled multi-category collections of images? While often a critical cue at the category-level, contour matches can be difficult to isolate reliably from edge clutter—even within labeled images from a known class, let alone unlabeled examples. We propose a shape discovery method in which local appearance (patch) matches serve to anchor the surrounding edge fragments, yielding a more reliable affinity function for images that accounts for both shape and appearance. Spectral clustering from the initial affinities provides candidate object clusters. Then, we compute the within-cluster match patterns to discern foreground edges from clutter, attributing higher weight to edges more likely to belong to a common object. In addition to discovering the object contours in each image, we show how to summarize what is found with prototypical shapes. Our results on benchmark datasets demonstrate the approach can successfully discover shapes from unlabeled images.
Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction
Authors: Longke Hu, Aixin Sun, Yong Liu
Venue: Conference SIGIR 2014
Year: 2014

Rating prediction is to predict the preference rating of a user to an item that she has not rated before. Using the business review data from Yelp, in this paper, we study business rating prediction. A business here can be a restaurant, a shopping mall or other kind of businesses. Different from most other types of items that have been studied in various recommender systems (e.g., movie, song, book), a business physically exists at a geographical location, and most businesses have geographical neighbors within walking distance. When a user visits a business, there is a good chance that she walks by its neighbors. Through data analysis, we observe that there exists weak positive correlation between a business's ratings and its neighbors' ratings, regardless of the categories of businesses. Based on this observation, we assume that a user's rating to a business is determined by both the intrinsic characteristics of the business and the extrinsic characteristics of its geographical neighbors. Using the widely adopted latent factor model for rating prediction, in our proposed solution, we use two kinds of latent factors to model a business: one for its intrinsic characteristics and the other for its extrinsic characteristics. The latter encodes the neighborhood influence of this business to its geographical neighbors. In our experiments, we show that by incorporating geographical neighborhood influences, much lower prediction error is achieved than the state-of-the-art models including Biased MF, SVD++, and Social MF. The prediction error is further reduced by incorporating influences from business category and review content.
GB-CENT: Gradient boosted categorical embedding and numerical trees
Authors: Qian Zhao, Yue Shi, Liangjie Hong
Venue: Conference WWW 2017
Year: 2017

Latent factor models and decision tree based models are widely used in tasks of prediction, ranking and recommendation. Latent factor models have the advantage of interpreting categorical features by a low-dimensional representation, while such an interpretation does not naturally fit numerical features. In contrast, decision tree based models enjoy the advantage of capturing the nonlinear interactions of numerical features, while their capability of handling categorical features is limited by the cardinality of those features. Since in real-world applications we usually have both abundant numerical features and categorical features with large cardinality (e.g. geolocations, IDs, tags etc.), we design a new model, called GB-CENT, which leverages latent factor embedding and tree components to achieve the merits of both while avoiding their demerits. With two real-world data sets, we demonstrate that GB-CENT can effectively (i.e. fast and accurately) achieve better accuracy than state-of-the-art matrix factorization, decision tree based models and their ensemble.
Deep embedding forest: Forest-based serving with deep embedding features
Authors: Jie Zhu, Ying Shan, J. C. Mao, Dong Yu, Holakou Rahmanian, Yi Zhang
Venue: Conference KDD 2017
Year: 2017

Deep Neural Networks (DNN) have demonstrated superior ability to extract high level embedding vectors from low level features. Despite the success, the serving time is still the bottleneck due to expensive run-time computation of multiple layers of dense matrices. GPGPU, FPGA, or ASIC-based serving systems require additional hardware that are not in the mainstream design of most commercial applications. In contrast, tree or forest-based models are widely adopted because of low serving cost, but heavily depend on carefully engineered features. This work proposes a Deep Embedding Forest model that benefits from the best of both worlds. The model consists of a number of embedding layers and a forest/tree layer. The former maps high dimensional (hundreds of thousands to millions) and heterogeneous low-level features to the lower dimensional (thousands) vectors, and the latter ensures fast serving. Built on top of a representative DNN model called Deep Crossing, and two forest/tree-based models including XGBoost and LightGBM, a two-step Deep Embedding Forest algorithm is demonstrated to achieve on-par or slightly better performance as compared with the DNN counterpart, with only a fraction of serving time on conventional hardware. After comparing with a joint optimization algorithm called partial fuzzification, also proposed in this paper, it is concluded that the two-step Deep Embedding Forest has achieved near optimal performance. Experiments based on large scale data sets (up to 1 billion samples) from a major sponsored search engine proves the efficacy of the proposed model.
Evaluation of background subtraction techniques for video surveillance
Authors: Sebastian Brutzer, Benjamin Höferlin, Gunther Heidemann
Venue: Conference CVPR 2011
Year: 2011

Background subtraction is one of the key techniques for automatic video analysis, especially in the domain of video surveillance. Although its importance, evaluations of recent background subtraction methods with respect to the challenges of video surveillance suffer from various shortcomings. To address this issue, we first identify the main challenges of background subtraction in the field of video surveillance. We then compare the performance of nine background subtraction methods with post-processing according to their ability to meet those challenges. Therefore, we introduce a new evaluation data set with accurate ground truth annotations and shadow masks. This enables us to provide precise in-depth evaluation of the strengths and drawbacks of background subtraction methods.
Solscsum: A linked sentence-comment dataset for social context summarization
Authors: Minh-Tien Nguyen, Chien-Xuan Tran, Duc-Vu Tran, Minh-Le Nguyen
Venue: Conference CIKM 2016
Year: 2016

This paper presents a dataset named SoLSCSum for social context summarization. The dataset includes 157 open-domain articles along with their comments collected from Yahoo News. The articles and their comments were manually annotated by two annotators to extract standard summaries. The inter-annotator agreement is 74.5% and Cohen's Kappa is 0.5845. To illustrate the potential use of our dataset, a learning to rank model was trained by using a set of local and cross features. Experimental results demonstrate that: (1) our model trained by Ranking SVM obtains significant improvements from 5.5% to 14.8% of ROUGE-1 over state-of-the-art baselines in document summarization and (2) our dataset can be used to train summary methods such as SVM.
Modeling disease progression via fused sparse group lasso
Authors: Jiayu Zhou, Jun Liu, Vaibhav A. Narayan, Jieping Ye
Venue: Conference KDD 2012
Year: 2012

Alzheimer's Disease (AD) is the most common neurodegenerative disorder associated with aging. Understanding how the disease progresses and identifying related pathological biomarkers for the progression is of primary importance in Alzheimer's disease research. In this paper, we develop novel multi-task learning techniques to predict the disease progression measured by cognitive scores and select biomarkers predictive of the progression. In multi-task learning, the prediction of cognitive scores at each time point is considered as a task, and multiple prediction tasks at different time points are performed simultaneously to capture the temporal smoothness of the prediction models across different time points. Specifically, we propose a novel convex fused sparse group Lasso (cFSGL) formulation that allows the simultaneous selection of a common set of biomarkers for multiple time points and specific sets of biomarkers for different time points using the sparse group Lasso penalty and in the meantime incorporates the temporal smoothness using the fused Lasso penalty. The proposed formulation is challenging to solve due to the use of several non-smooth penalties. We show that the proximal operator associated with the proposed formulation exhibits a certain decomposition property and can be computed efficiently; thus cFSGL can be solved efficiently using the accelerated gradient method. To further improve the model, we propose two non-convex formulations to reduce the shrinkage bias inherent in the convex formulation. We employ the difference of convex programming technique to solve the non-convex formulations. Our extensive experiments using data from the Alzheimer's Disease Neuroimaging Initiative demonstrate the effectiveness of the proposed progression models in comparison with existing methods for disease progression. We also perform longitudinal stability selection to identify and analyze the temporal patterns of biomarkers in disease progression.
Survival analysis based framework for early prediction of student dropouts
Authors: Sattar Ameri, Mahtab Jahanbani Fard, Ratna Babu Chinnam, Chandan K. Reddy
Venue: Conference CIKM 2016
Year: 2016

Retention of students at colleges and universities has been a concern among educators for many decades. The consequences of student attrition are significant for students, academic staffs and the universities. Thus, increasing student retention is a long term goal of any academic institution. The most vulnerable students are the freshman, who are at the highest risk of dropping out at the beginning of their study. Therefore, the early identification of {\emph{``at-risk''}} students is a crucial task that needs to be effectively addressed. In this paper, we develop a survival analysis framework for early prediction of student dropout using Cox proportional hazards regression model (Cox). We also applied time-dependent Cox (TD-Cox), which captures time-varying factors and can leverage those information to provide more accurate prediction of student dropout. For this prediction task, our model utilizes different groups of variables such as demographic, family background, financial, high school information, college enrollment and semester-wise credits. The proposed framework has the ability to address the challenge of predicting dropout students as well as the semester that the dropout will occur. This study enables us to perform proactive interventions in a prioritized manner where limited academic resources are available. This is critical in the student retention problem because not only correctly classifying whether a student is going to dropout is important but also when this is going to happen is crucial for a focused intervention. We evaluate our method on real student data collected at Wayne State University. Results show that the proposed Cox-based framework can predict the student dropouts and semester of dropout with high accuracy and precision compared to the other state-of-the-art methods.
Recommending investors for crowdfunding projects
Authors: Jisun An, Daniele Quercia, Jon Crowcroft
Venue: Conference WWW 2014
Year: 2014

To bring their innovative ideas to market, those embarking in new ventures have to raise money, and, to do so, they have often resorted to banks and venture capitalists. Nowadays, they have an additional option: that of crowdfunding. The name refers to the idea that funds come from a network of people on the Internet who are passionate about supporting others' projects. One of the most popular crowdfunding sites is Kickstarter. In it, creators post descriptions of their projects and advertise them on social media sites (mainly Twitter), while investors look for projects to support. The most common reason for project failure is the inability of founders to connect with a sufficient number of investors, and that is mainly because hitherto there has not been any automatic way of matching creators and investors. We thus set out to propose different ways of recommending investors found on Twitter for specific Kickstarter projects. We do so by conducting hypothesis-driven analyses of pledging behavior and translate the corresponding findings into different recommendation strategies. The best strategy achieves, on average, 84% of accuracy in predicting a list of potential investors' Twitter accounts for any given project. Our findings also produced key insights about the whys and wherefores of investors deciding to support innovative efforts.