OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic factual summaries for entity cards
Authors: Faegheh Hasibi, Krisztian Balog, Svein Erik Bratsberg
Venue: Conference SIGIR 2017
Year: 2017

Entity cards are being used frequently in modern web search engines to offer a concise overview of an entity directly on the results page. These cards are composed of various elements, one of them being the entity summary: a selection of facts describing the entity from an underlying knowledge base. These summaries, while presenting a synopsis of the entity, can also directly address users' information needs. In this paper, we make the first effort towards generating and evaluating such factual summaries. We introduce and address the novel problem of dynamic entity summarization for entity cards, and break it down to two specific subtasks: fact ranking and summary generation. We perform an extensive evaluation of our method using crowdsourcing. Our results show the effectiveness of our fact ranking approach and validate that users prefer dynamic summaries over static ones.
Getting more for less: Optimized crowdsourcing with dynamic tasks and goals
Authors: Ari Kobren, Chun How Tan, Panagiotis G. Ipeirotis, Evgeniy Gabrilovich
Venue: Conference WWW 2015
Year: 2015

In crowdsourcing systems, the interests of contributing participants and system stakeholders are often not fully aligned. Participants seek to learn, be entertained, and perform easy tasks, which offer them instant gratification; system stakeholders want users to complete more difficult tasks, which bring higher value to the crowdsourced application. We directly address this problem by presenting techniques that optimize the crowdsourcing process by jointly maximizing the user longevity in the system and the true value that the system derives from user participation.
Crowdselect: Increasing accuracy of crowdsourcing tasks through behavior prediction and user selection
Authors: Chenxi Qiu, Anna Cinzia Squicciarini, Barbara Carminati, James Caverlee, Dev Rishi Khare
Venue: Conference CIKM 2016
Year: 2016

Crowdsourcing allows many people to complete tasks of various difficulty with minimal recruitment and administration costs. However, the lack of participant accountability may entice people to complete as many tasks as possible without fully engaging in them, jeopardizing the quality of responses. In this paper, we present a dynamic and time efficient solution to the task assignment problem in crowdsourcing platforms. Our proposed approach, CrowdSelect, offers a theoretically proven algorithm to assign workers to tasks in a cost efficient manner, while ensuring high accuracy of the overall task. In contrast to existing works, our approach makes minimal assumptions on the probability of error for workers, and completely removes the assumptions that such probability is known apriori and that it remains consistent over time. Through experiments over real Amazon Mechanical Turk traces and synthetic data, we find that CrowdSelect has a significant gain in term of accuracy compared to state-of-the-art algorithms, and can provide a 17.5\% gain in answers' accuracy compared to previous methods, even when there are over 50\% malicious workers.
A fast trajectory outlier detection approach via driving behavior modeling
Authors: Hao Wu, Weiwei Sun, Baihua Zheng
Venue: Conference CIKM 2017
Year: 2017

Trajectory outlier detection is a fundamental building block for many location-based service (LBS) applications, with a large application base. We dedicate this paper on detecting the outliers from vehicle trajectories efficiently and effectively. In addition, we want our solution to be able to issue an alarm early when an outlier trajectory is only partially observed (i.e., the trajectory has not yet reached the destination). Most existing works study the problem on general Euclidean trajectories and require accesses to the historical trajectory database or computations on the distance metric that are very expensive. Furthermore, few of existing works consider some specific characteristics of vehicles trajectories (e.g., their movements are constrained by the underlying road networks), and majority of them require the input of complete trajectories. Motivated by this, we propose a vehicle outlier detection approach namely DB-TOD which is based on probabilistic model via modeling the driving behavior/preferences from the set of historical trajectories. We design outlier detection algorithms on both complete trajectory and partial one. Our probabilistic model-based approach makes detecting trajectory outlier extremely efficient while preserving the effectiveness, contributed by the relatively accurate model on driving behavior. We conduct comprehensive experiments using real datasets and the results justify both effectiveness and efficiency of our approach.
Not all links are created equal: An adaptive embedding approach for social personalized ranking
Authors: Qing Zhang, Houfeng Wang
Venue: Conference SIGIR 2016
Year: 2016

With a large amount of complex network data available, most existing recommendation models consider exploiting rich user social relations for better interest targeting. In these approaches, the underlying assumption is that similar users in social networks would prefer similar items. However, in practical scenarios, social link may not be formed by common interest. For example, one general collected social network might be used for various specific recommendation scenarios. The problem of noisy social relations without interest relevance will arise to hurt the performance. Moreover, the sparsity problem of social network makes it much more challenging, due to the two-fold problem needed to be solved simultaneously, for effectively incorporating social information to benefit recommendation. To address this challenge, we propose an adaptive embedding approach to solve the both jointly for better recommendation in real world setting. Experiments conducted on real world datasets show that our approach outperforms current methods.
Unifying inter-region autocorrelation and intra-region structures for spatial embedding via collective adversarial learning
Authors: Yunchao Zhang, Yanjie Fu, Pengyang Wang, Xiaolin Li, Yu Zheng
Venue: Conference KDD 2019
Year: 2019

Unsupervised spatial representation learning aims to automatically identify effective features of geographic entities (i.e., regions) from unlabeled yet structural geographical data. Existing network embedding methods can partially address the problem by: (1) regarding a region as a node in order to reformulate the problem into node embedding; (2) regarding a region as a graph in order to reformulate the problem into graph embedding. However, these studies can be improved by preserving (1) intra-region geographic structures, which are represented by multiple spatial graphs, leading to a reformulation of collective learning from relational graphs; (2) inter-region spatial autocorrelations, which are represented by pairwise graph regularization, leading to a reformulation of adversarial learning. Moreover, field data in real systems are usually lack of labels, an unsupervised fashion helps practical deployments. Along these lines, we develop an unsupervised Collective Graph-regularized dual-Adversarial Learning (CGAL) framework for multi-view graph representation learning and also a Graph-regularized dual-Adversarial Learning (GAL) framework for single-view graph representation learning. Finally, our experimental results demonstrate the enhanced effectiveness of our method.
MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching
Authors: Jiafeng Guo, Yixing Fan, Xiang Ji, Xueqi Cheng
Venue: Conference SIGIR 2019
Year: 2019

Text matching is the core problem in many natural language processing (NLP) tasks, such as information retrieval, question answering, and conversation. Recently, deep leaning technology has been widely adopted for text matching, making neural text matching a new and active research domain. With a large number of neural matching models emerging rapidly, it becomes more and more difficult for researchers, especially those newcomers, to learn and understand these new models. Moreover, it is usually difficult to try these models due to the tedious data pre-processing, complicated parameter configuration, and massive optimization tricks, not to mention the unavailability of public codes sometimes. Finally, for researchers who want to develop new models, it is also not an easy task to implement a neural text matching model from scratch, and to compare with a bunch of existing models. In this paper, therefore, we present a novel system, namely MatchZoo, to facilitate the learning, practicing and designing of neural text matching models. The system consists of a powerful matching library and a user-friendly and interactive studio, which can help researchers: 1) to learn state-of-the-art neural text matching models systematically, 2) to train, test and apply these models with simple configurable steps; and 3) to develop their own models with rich APIs and assistance.
Modeling and Aggregation of Complex Annotations via Annotation Distances
Authors: Alexander Braylan, Matthew Lease
Venue: Conference WWW 2020
Year: 2020

Modeling annotators and their labels is valuable for ensuring collected data quality. Though many models have been proposed for binary or categorical labels, prior methods do not generalize to complex annotations (e.g., open-ended text, multivariate, or structured responses) without devising new models for each specific task. To obviate the need for task-specific modeling, we propose to model distances between labels, rather than the labels themselves. Our models are largely agnostic to the distance function; we leave it to the requesters to specify an appropriate distance function for their given annotation task. We propose three models of annotation quality, including a Bayesian hierarchical extension of multidimensional scaling which can be trained in an unsupervised or semi-supervised manner. Results show the generality and effectiveness of our models across diverse complex annotation tasks: sequence labeling, translation, syntactic parsing, and ranking.
Debiasing crowdsourced batches
Authors: Honglei Zhuang, Aditya G. Parameswaran, Dan Roth, Jiawei Han
Venue: Conference KDD 2015
Year: 2015

Crowdsourcing is the de-facto standard for gathering annotated data. While, in theory, data annotation tasks are assumed to be attempted by workers independently, in practice, data annotation tasks are often grouped into batches to be presented and annotated by workers together, in order to save on the time or cost overhead of providing instructions or necessary background. Thus, even though independence is usually assumed between annotations on data items within the same batch, in most cases, a worker's judgment on a data item can still be affected by other data items within the batch, leading to additional errors in collected labels. In this paper, we study the data annotation bias when data items are presented as batches to be judged by workers simultaneously. We propose a novel worker model to characterize the annotating behavior on data batches, and present how to train the worker model on annotation data sets. We also present a debiasing technique to remove the effect of such annotation bias from adversely affecting the accuracy of labels obtained. Our experimental results on both synthetic data and real-world data demonstrate the effectiveness of our proposed method.
Mining-based compression approach of propositional formulae
Authors: Saïd Jabbour, Lakhdar Sais, Yakoub Salhi, Takeaki Uno
Venue: Conference CIKM 2013
Year: 2013

In this paper, we propose a first application of data mining techniques to propositional satisfiability. Our proposed mining based compression approach aims to discover and to exploit hidden structural knowledge for reducing the size of propositional formulae in conjunctive normal form (CNF). It combines both frequent itemset mining techniques and Tseitin's encoding for a compact representation of CNF formulae. The experimental evaluation of our approach shows interesting reductions of the sizes of many application instances taken from the last SAT competitions.
Online selection of diverse results
Authors: Debmalya Panigrahi, Atish Das Sarma, Gagan Aggarwal, Andrew Tomkins
Venue: Conference WSDM 2012
Year: 2012

The phenomenal growth in the volume of easily accessible information via various web-based services has made it essential for service providers to provide users with personalized representative summaries of such information. Further, online commercial services including social networking and micro-blogging websites, e-commerce portals, leisure and entertainment websites, etc. recommend interesting content to users that is simultaneously diverse on many different axes such as topic, geographic specificity, etc. The key algorithmic question in all these applications is the generation of a succinct, representative, and relevant summary from a large stream of data coming from a variety of sources. In this paper, we formally model this optimization problem, identify its key structural characteristics, and use these observations to design an extremely scalable and efficient algorithm. We analyze the algorithm using theoretical techniques to show that it always produces a nearly optimal solution. In addition, we perform large-scale experiments on both real-world and synthetically generated datasets, which confirm that our algorithm performs even better than its analytical guarantees in practice, and also outperforms other candidate algorithms for the problem by a wide margin.
Implementing optimal outcomes in social computing: A game-theoretic approach
Authors: Arpita Ghosh, Patrick Hummel
Venue: Conference WWW 2012
Year: 2012

In many social computing applications such as online Q&A forums, the best contribution for each task receives some high reward, while all remaining contributions receive an identical, lower reward irrespective of their actual qualities. Suppose a mechanism designer (site owner) wishes to optimize an objective that is some function of the number and qualities of received contributions. When potential contributors are {\em strategic} agents, who decide whether to contribute or not to selfishly maximize their own utilities, is such a "best contribution" mechanism, Mb, adequate to implement an outcome that is optimal for the mechanism designer? We first show that in settings where a contribution's value is determined primarily by an agent's expertise, and agents only strategically choose whether to contribute or not, contests can implement optimal outcomes: for any reasonable objective, the rewards for the best and remaining contributions in Mb can always be chosen so that the outcome in the unique symmetric equilibrium of Mb maximizes the mechanism designer's utility. We also show how the mechanism designer can learn these optimal rewards when she does not know the parameters of the agents' utilities, as might be the case in practice. We next consider settings where a contribution's value depends on both the contributor's expertise as well as her effort, and agents endogenously choose how much effort to exert in addition to deciding whether to contribute. Here, we show that optimal outcomes can never be implemented by contests if the system can rank the qualities of contributions perfectly. However, if there is noise in the contributions' rankings, then the mechanism designer can again induce agents to follow strategies that maximize his utility. Thus imperfect rankings can actually help achieve implementability of optimal outcomes when effort is endogenous and influences quality.
Spotlight: Detecting anomalies in streaming graphs
Authors: Dhivya Eswaran, Christos Faloutsos, Sudipto Guha, Nina Mishra
Venue: Conference KDD 2018
Year: 2018

How do we spot interesting events from e-mail or transportation logs? How can we detect port scan or denial of service attacks from IP-IP communication data? In general, given a sequence of weighted, directed or bipartite graphs, each summarizing a snapshot of activity in a time window, how can we spot anomalous graphs containing the sudden appearance or disappearance of large dense subgraphs (e.g., near bicliques) in near real-time using sublinear memory? To this end, we propose a randomized sketching-based approach called SpotLight, which guarantees that an anomalous graph is mapped 'far' away from 'normal' instances in the sketch space with high probability for appropriate choice of parameters. Extensive experiments on real-world datasets show that SpotLight (a) improves accuracy by at least 8.4% compared to prior approaches, (b) is fast and can process millions of edges within a few minutes, (c) scales linearly with the number of edges and sketching dimensions and (d) leads to interesting discoveries in practice.
Robust contextual outlier detection: Where context meets sparsity
Authors: Jiongqian Liang, Srinivasan Parthasarathy
Venue: Conference CIKM 2016
Year: 2016

Outlier detection is a fundamental data science task with applications ranging from data cleaning to network security. Given the fundamental nature of the task, this has been the subject of much research. Recently, a new class of outlier detection algorithms has emerged, called {\it contextual outlier detection}, and has shown improved performance when studying anomalous behavior in a specific context. However, as we point out in this article, such approaches have limited applicability in situations where the context is sparse (i.e. lacking a suitable frame of reference). Moreover, approaches developed to date do not scale to large datasets. To address these problems, here we propose a novel and robust approach alternative to the state-of-the-art called RObust Contextual Outlier Detection (ROCOD). We utilize a local and global behavioral model based on the relevant contexts, which is then integrated in a natural and robust fashion. We also present several optimizations to improve the scalability of the approach. We run ROCOD on both synthetic and real-world datasets and demonstrate that it outperforms other competitive baselines on the axes of efficacy and efficiency (40X speedup compared to modern contextual outlier detection methods). We also drill down and perform a fine-grained analysis to shed light on the rationale for the performance gains of ROCOD and reveal its effectiveness when handling objects with sparse contexts.
Experiments with non-parametric topic models
Authors: Wray L. Buntine, Swapnil Mishra
Venue: Conference KDD 2014
Year: 2014

In topic modelling, various alternative priors have been developed, for instance asymmetric and symmetric priors for the document-topic and topic-word matrices respectively, the hierarchical Dirichlet process prior for the document-topic matrix and the hierarchical Pitman-Yor process prior for the topic-word matrix. For information retrieval, language models exhibiting word burstiness are important. Indeed, this burstiness effect has been show to help topic models as well, and this requires additional word probability vectors for each document. Here we show how to combine these ideas to develop high-performing non-parametric topic models exhibiting burstiness based on standard Gibbs sampling. Experiments are done to explore the behavior of the models under different conditions and to compare the algorithms with previously published. The full non-parametric topic models with burstiness are only a small factor slower than standard Gibbs sampling for LDA and require double the memory, making them very competitive. We look at the comparative behaviour of different models and present some experimental insights.
Kate: K-competitive autoencoder for text
Authors: Yu Chen, Mohammed J. Zaki
Venue: Conference KDD 2017
Year: 2017

Autoencoders have been successful in learning meaningful representations from image datasets. However, their performance on text datasets has not been widely studied. Traditional autoencoders tend to learn possibly trivial representations of text documents due to their confoundin properties such as high-dimensionality, sparsity and power-law word distributions. In this paper, we propose a novel k-competitive autoencoder, called KATE, for text documents. Due to the competition between the neurons in the hidden layer, each neuron becomes specialized in recognizing specific data patterns, and overall the model can learn meaningful representations of textual data. A comprehensive set of experiments show that KATE can learn better representations than traditional autoencoders including denoising, contractive, variational, and k-sparse autoencoders. Our model also outperforms deep generative models, probabilistic topic models, and even word representation models (e.g., Word2Vec) in terms of several downstream tasks such as document classification, regression, and retrieval.
Recwalk: Nearly uncoupled random walks for top-n recommendation
Authors: Athanasios N. Nikolakopoulos, George Karypis
Venue: Conference WSDM 2019
Year: 2019

Random walks can provide a powerful tool for harvesting the rich network of interactions captured within item-based models for top-n recommendation. They can exploit indirect relations between the items, mitigate the effects of sparsity, ensure wider itemspace coverage, as well as increase the diversity of recommendation lists. Their potential however, is hindered by the tendency of the walks to rapidly concentrate towards the central nodes of the graph, thereby significantly restricting the range of K-step distributions that can be exploited for personalized recommendations. In this work we introduce RecWalk; a novel random walk-based method that leverages the spectral properties of nearly uncoupled Markov chains to provably lift this limitation and prolong the influence of users' past preferences on the successive steps of the walk--allowing the walker to explore the underlying network more fruitfully. A comprehensive set of experiments on real-world datasets verify the theoretically predicted properties of the proposed approach and indicate that they are directly linked to significant improvements in top-n recommendation accuracy. They also highlight RecWalk's potential in providing a framework for boosting the performance of item-based models. RecWalk achieves state-of-the-art top-n recommendation quality outperforming several competing approaches, including recently proposed methods that rely on deep neural networks.
CompactETA: A fast inference system for travel time prediction
Authors: Kun Fu 0002, Fanlin Meng, Jieping Ye, Zheng Wang 0010
Venue: Conference KDD 2020
Year: 2020

Computing estimated time of arrival (ETA) is one of the most important services for online ride-hailing platforms like DiDi and Uber. With billions of service queries per day on such platforms, a fast inference ETA module ensures the efficiency of the overall decision system to guarantee satisfied user experience, as well as saving significant operating cost. In this paper, we develop a novel ETA learning system named as CompactETA, which provides an accurate online travel time inference within 100 microseconds. In the proposed method, we encode high order spatial and temporal dependency into sophisticated representations by applying graph attention network on a spatiotemporal weighted road network graph. We further encode the sequential information of the travel route by positional encoding to avoid the recurrent network structure. The properly learnt representations enable us to apply a very simple multi-layer perceptron model for online real-time inference. Evaluation of both offline experiments and online A/B testing verifies that CompactETA reduces the inference latency by more than 100 times compared to a state-of-the-art system, while maintains competing prediction accuracy.