OpenRaiser/Intern-Atlas huggingface dataset:
A framework for summarizing and analyzing twitter feeds
Authors: Xintian Yang, Amol Ghoting, Yiye Ruan, Srinivasan Parthasarathy
Venue: Conference KDD 2012
Year: 2012

The firehose of data generated by users on social networking and microblogging sites such as Facebook and Twitter is enormous. Real-time analytics on such data is challenging with most current efforts largely focusing on the efficient querying and retrieval of data produced recently. In this paper, we present a dynamic pattern driven approach to summarize data produced by Twitter feeds. We develop a novel approach to maintain an in-memory summary while retaining sufficient information to facilitate a range of user-specific and topic-specific temporal analytics. We empirically compare our approach with several state-of-the-art pattern summarization approaches along the axes of storage cost, query accuracy, query flexibility, and efficiency using real data from Twitter. We find that the proposed approach is not only scalable but also outperforms existing approaches by a large margin.
Threshold behavior of incentives in social networks
Authors: Nagaraj Kota, Y. Narahari
Venue: Conference CIKM 2010
Year: 2010

The advent of large scale online social networks has resulted in a spurt of studies on the user participation in the networks. We consider a query incentive model on social networks, where user's queries are answered through her friendship network and there are `rewards' or `incentives' in the system to answer the queries utilizing ones community. We model the friendship network as a random graph with power-law degree distribution, and show that the reward function exhibits a theoretic threshold behavior on the scaling exponent α, a network parameter. Specifically, there exists a threshold on α above which the reward is exponential in the average path length in the network and below the threshold, the reward is proportional to the average path length. We demonstrate this finding on simulated power-law networks and real world data gathered from online social media such as Flickr, Digg, YouTube and Yahoo! Answers.
Learning interactions for social prediction in large-scale networks
Authors: Xiaofeng Yu, Junqing Xie
Venue: Conference CIKM 2014
Year: 2014

Social networks have already emerged as inconceivably vast information repositories and have provided great opportunities for social connection and information diffusion. In light of these notable outcomes, social prediction is a critical research goal for analyzing and understanding social media and online social networks. We investigate underlying social theories that drive the characteristics and dynamics of social networks, including homophily, heterophily, and the structural hole theories. We propose a unified coherent framework, namely mutual latent random graphs (MLRGs), to exploit mutual interactions and benefits for predicting social actions (e.g., users' behaviors, opinions, preferences or interests) and discovering social ties (e.g., multiple labeled relationships between users) simultaneously in large-scale social networks. MLRGs introduce latent, or hidden factors and coupled models with users, users' actions and users' ties to flexibly encode evidences from both sources. We propose an approximate optimization algorithm to learn the model parameters efficiently. Furthermore, we speedup this algorithm based on the Hadoop MapReduce framework to handle large-scale social networks. We performed experiments on two real-world social networking datasets to demonstrate the validity and competitiveness of our approach.
A multi-scale hybrid linear model for lossy image representation
Authors: Wei Hong 0003, John Wright 0001, Kun Huang 0001, Yi Ma 0001
Venue: Conference ICCV 2005
Year: 2005

This paper introduces a simple and efficient representation for natural images. We partition an image into blocks and treat the blocks as vectors in a high-dimensional space. We then fit a piecewise linear model (i.e. a union of affine subspaces) to the vectors at each down-sampling scale. We call this a multiscale hybrid linear model of the image. The hybrid and hierarchical structure of this model allows us effectively to extract and exploit multimodal correlations among the imagery data at different scales. It conceptually and computationally remedies limitations of many existing image representation methods that are based on either a fixed linear transformation (e.g. DCT, wavelets), an adaptive unimodal linear transformation (e.g. PCA), or a multi-modal model at a single scale. We will justify both analytically and experimentally why and how such a simple multiscale hybrid model is able to reduce simultaneously the model complexity and computational cost. Despite a small overhead for the model, our results show that this new model gives more compact representations for a wide variety of natural images under a wide range of signal-to-noise ratio than many existing methods, including wavelets.
Inferring and exploiting categories for next location prediction
Authors: Ankita Likhyani, Deepak Padmanabhan, Srikanta J. Bedathur, Sameep Mehta
Venue: Conference WWW 2015
Year: 2015

Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called "check-in" profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.
Context-adaptive matrix factorization for multi-context recommendation
Authors: Tong Man, Huawei Shen, Junming Huang, Xueqi Cheng
Venue: Conference CIKM 2015
Year: 2015

Data sparsity is a long-standing challenge for recommender systems based on collaborative filtering. A promising solution for this problem is multi-context recommendation, i.e., leveraging users' explicit or implicit feedback from multiple contexts. In multi-context recommendation, various types of interactions between entities (users and items) are combined to alleviate data sparsity of a single context in a collective manner. Two issues are crucial for multi-context recommendation: (1) How to differentiate context-specific factors from entity-intrinsic factors shared across contexts? (2) How to capture the salient phenomenon that some entities are insensitive to contexts while others are remarkably context-dependent? Previous methods either do not consider context-specific factors, or assume that a context imposes equal influence on different entities, limiting their capability of combating data sparsity problem by taking full advantage of multiple contexts.
Using social annotations to enhance document representation for personalized search
Authors: Mohamed Reda Bouadjenek, Hakim Hacid, Mokrane Bouzeghoub, Athena Vakali
Venue: Conference SIGIR 2013
Year: 2013

In this paper, we present a contribution to IR modeling. We propose an approach that computes on the fly, a Personalized Social Document Representation (PSDR) of each document per user based on his social activities. The PSDRs are used to rank documents with respect to a query. This approach has been intensively evaluated on a large public dataset, showing significant benefits for personalized search.
Ranking and classifying attractiveness of photos in folksonomies
Authors: José San Pedro, Stefan Siersdorfer
Venue: Conference WWW 2009
Year: 2009

Web 2.0 applications like Flickr, YouTube, or Del.icio.us are increasingly popular online communities for creating, editing and sharing content. The growing size of these folksonomies poses new challenges in terms of search and data mining. In this paper we introduce a novel methodology for automatically ranking and classifying photos according to their attractiveness for folksonomy members. To this end, we exploit image features known for having significant effects on the visual quality perceived by humans (e.g. sharpness and colorfulness) as well as textual meta data, in what is a multi-modal approach. Using feedback and annotations available in the Web 2.0 photo sharing system Flickr, we assign relevance values to the photos and train classification and regression models based on these relevance assignments. With the resulting machine learning models we categorize and rank photos according to their attractiveness. Applications include enhanced ranking functions for search and recommender methods for attractive content. Large scale experiments on a collection of Flickr photos demonstrate the viability of our approach.
Deep character-level click-through rate prediction for sponsored search
Authors: Bora Edizel, Amin Mantrach, Xiao Bai
Venue: Conference SIGIR 2017
Year: 2017

Predicting the click-through rate of an advertisement is a critical component of online advertising platforms. In sponsored search, the click-through rate estimates the probability that a displayed advertisement is clicked by a user after she submits a query to the search engine. Commercial search engines typically rely on machine learning models trained with a large number of features to make such predictions. This inevitably requires a lot of engineering efforts to define, compute, and select the appropriate features. In this paper, we propose two novel approaches (one working at character level and the other working at word level) that use deep convolutional neural networks to predict the click-through rate of a query-advertisement pair. Specifically, the proposed architectures only consider the textual content appearing in a query-advertisement pair as input, and produce as output a click-through rate prediction. By comparing the character-level model with the word-level model, we show that language representation can be learnt from scratch at character level when trained on enough data. Through extensive experiments using billions of query-advertisement pairs of a popular commercial search engine, we demonstrate that both approaches significantly outperform a baseline model built on well-selected text features and a state-of-the-art word2vec-based approach. Finally, by combining the predictions of the deep models introduced in this study with the prediction of the model in production of the same commercial search engine, we significantly improve the accuracy and the calibration of the click-through rate prediction of the production system.
Predicting short-term public transport demand via inhomogeneous Poisson processes
Authors: Aditya Krishna Menon, Young Lee
Venue: Conference CIKM 2017
Year: 2017

Forecasting short term passenger demand for public transport is a core problem in urban mobility. Typically, this is addressed using Poisson regression or homogeneous Poisson processes. However, such approaches have several limitations, including susceptibility to noise at fine time granularities, and the inability to capture complex non-stationary trends. In this paper, we show how such short term demand can be accurately modelled with an inhomogeneous Poisson process, using a neural network as the underlying intensity. This choice of intensity subsumes existing models as special cases, and is powerful enough to capture certain stylised facts of real-world demand. Experiments on real-world bus arrival data from a large metropolitan area in Australia validate our approach.
Regime shifts in streams: Real-time forecasting of co-evolving time sequences
Authors: Yasuko Matsubara, Yasushi Sakurai
Venue: Conference KDD 2016
Year: 2016

Given a large, online stream of multiple co-evolving event sequences, such as sensor data and Web-click logs, that contains various types of non-linear dynamic evolving patterns of different durations, how can we efficiently and effectively capture important patterns? How do we go about forecasting long-term future events? In this paper, we present REGIMECAST, an efficient and effective method for forecasting co-evolving data streams. REGIMECAST is designed as an adaptive non-linear dynamical system, which is inspired by the concept of "regime shifts" in natural dynamical systems. Our method has the following properties: (a) Effective: it operates on large data streams, captures important patterns and performs long-term forecasting; (b) Adaptive: it automatically and incrementally recognizes the latent trends and dynamic evolution patterns (i.e., regimes) that are unknown in advance; (c) Scalable: it is fast and the computation cost does not depend on the length of data streams; (d) Any-time: it provides a response at any time and generates long-range future events. Extensive experiments on real datasets demonstrate that REGIMECAST does indeed make long-range forecasts, and it outperforms state-of-the-art competitors as regards accuracy and speed.
Understanding behaviors that lead to purchasing: A case study of pinterest
Authors: Caroline Lo, Dan Frankowski, Jure Leskovec
Venue: Conference KDD 2016
Year: 2016

Online e-commerce applications are becoming a primary vehicle for people to find, compare, and ultimately purchase products. One of the fundamental questions that arises in e-commerce is to characterize, understand, and model user long-term purchasing intent, which is important as it allows for personalized and context relevant e-commerce services.
Learning under feature drifts in textual streams
Authors: Damianos P. Melidis, Myra Spiliopoulou, Eirini Ntoutsi
Venue: Conference CIKM 2018
Year: 2018

Huge amounts of textual streams are generated nowadays, especially in social networks like Twitter and Facebook. As the discussion topics and user opinions on those topics change drastically with time, those streams undergo changes in data distribution, leading to changes in the concept to be learned, a phenomenon called concept drift. One particular type of drift, that has not yet attracted a lot of attention is feature drift, i.e., changes in the features that are relevant for the learning task at hand. In this work, we propose an approach for handling feature drifts in textual streams. Our approach integrates i) an ensemble-based mechanism to accurately predict the feature/word values for the next time-point by taking into account the different features might be subject to different temporal trends and ii) a sketch-based feature space maintenance mechanism that allows for a memory-bounded maintenance of the feature space over the stream. Experiments with textual streams from the sentiment analysis, email preference and spam detection demonstrate that our approach achieves significantly better or competitive performance compared to baselines.
PARL: let strangers speak out what you like
Authors: Libing Wu, Cong Quan, Chenliang Li, Donghong Ji
Venue: Conference CIKM 2018
Year: 2018

Review-based methods are one of the dominant methods to address the data sparsity problem of recommender system. However, the performance of most existing review-based methods will degrade when the review is also sparse. To this end, we propose a method to exploit user-item p air-dependent features from a uxiliary r eviews written by l ike-minded users (PARL) to address such problem. That is, both the reviews written by the user and the reviews written for the item are incorporated to highlight the useful features covered by the auxiliary reviews. PARL not only alleviates the sparsity problem of reviews but also produce extra informative features to further improve the accuracy of rating prediction. More importantly, it is designed as a plug-and-play model which can be plugged into various deep recommender systems to improve recommendations provided by them. Extensive experiments on five real-world datasets show that PARL achieves better prediction accuracy than other state-of-the-art alternatives. Also, with the exploitation of auxiliary reviews, the performance of PARL is robust on datasets with different characteristics.
Nonlinear adaptive distance metric learning for clustering
Authors: Jianhui Chen, Zheng Zhao, Jieping Ye, Huan Liu
Venue: Conference KDD 2007
Year: 2007

A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a low-dimensional manifold, where geometric relationships such as pairwise distances are preserved. It can be extended to the nonlinear case by applying the kernel trick, which embeds the data into a feature space by specifying the kernel function that computes the dot products between data points in the feature space. In this paper, we propose a novel unsupervised Nonlinear Adaptive Metric Learning algorithm, called NAML, which performs clustering and distance metric learning simultaneously. NAML firstmaps the data to a high-dimensional space through a kernel function; then applies a linear projection to find a low-dimensional manifold where the separability of the data is maximized; and finally performs clustering in the low-dimensional space. The performance of NAML depends on the selection of the kernel function and the projection. We show that the joint kernel learning, dimensionality reduction, and clustering can be formulated as a trace maximization problem, which can be solved via an iterative procedure in the EM framework. Experimental results demonstrated the efficacy of the proposed algorithm.
Three-dimensional stable matching problem for spatial crowdsourcing platforms
Authors: Boyang Li, Yurong Cheng, Ye Yuan, Guoren Wang, Lei Chen
Venue: Conference KDD 2019
Year: 2019

The popularity of mobile Internet techniques and Online-To-Offline(O2O) business models has led to the emergence of various spatial crowdsourcing (SC) platforms in our daily life. A core issue of SC platforms is to assign tasks to suitable crowd workers. Existing approaches usually focus on the matching of two types of objects,tasks and workers, and let workers to travel to the location of usersto provide services, which is a 2D matching problem. However, recent services provided by some new platforms, such as person-alized haircut service1and station ride-sharing, need users andworkers travel together to a third workplace to complete the service, which is indeed a 3D matching problem. Approaches in the existingstudies either cannot solve such 3D matching problem, or lack aassignment plan satisfying both users' and workers' preference inreal applications. Thus, in this paper, we propose a 3-Dimensional Stable Spatial Matching(3D-SSM) for the 3D matching problem innew SC services. We prove that the 3D-SSM problem is NP-hard, and propose two baseline algorithms and two efficient approximatealgorithms with bounded approximate ratios to solve it. Finally, weconduct extensive experiment studies which verify the efficiencyand effectiveness of the proposed algorithms on real and synthetic datasets.
Multirank: co-ranking for objects and relations in multi-relational data
Authors: Michael Kwok-Po Ng, Xutao Li, Yunming Ye
Venue: Conference KDD 2011
Year: 2011

The main aim of this paper is to design a co-ranking scheme for objects and relations in multi-relational data. It has many important applications in data mining and information retrieval. However, in the literature, there is a lack of a general framework to deal with multi-relational data for co-ranking. The main contribution of this paper is to (i) propose a framework (MultiRank) to determine the importance of both objects and relations simultaneously based on a probability distribution computed from multi-relational data; (ii) show the existence and uniqueness of such probability distribution so that it can be used for co-ranking for objects and relations very effectively; and (iii) develop an efficient iterative algorithm to solve a set of tensor (multivariate polynomial) equations to obtain such probability distribution. Extensive experiments on real-world data suggest that the proposed framework is able to provide a co-ranking scheme for objects and relations successfully. Experimental results have also shown that our algorithm is computationally efficient, and effective for identification of interesting and explainable co-ranking results.
On ridesharing competition and accessibility: Evidence from uber, lyft, and taxi
Authors: Shan Jiang, Le Chen, Alan Mislove, Christo Wilson
Venue: Conference WWW 2018
Year: 2018

Ridesharing services such as Uber and Lyft have become an important part of the Vehicle For Hire (VFH) market, which used to be dominated by taxis. Unfortunately, ridesharing services are not required to share data like taxi services, which has made it challenging to compare the competitive dynamics of these services, or assess their impact on cities. In this paper, we comprehensively compare Uber, Lyft, and taxis with respect to key market features (supply, demand, price, and wait time) in San Francisco and New York City. Based on point pattern statistics, we develop novel statistical techniques to validate our measurement methods. Using spatial lag models, we investigate the accessibility of VFH services, and find that transportation infrastructure and socio-economic features have substantial effects on VFH market features.
Crowdsourcing-based urban anomaly prediction system for smart cities
Authors: Chao Huang, Xian Wu, Dong Wang
Venue: Conference CIKM 2016
Year: 2016

Crowdsourcing has become an emerging data collection paradigm for smart city applications. A new category of crowdsourcing-based urban anomaly reporting systems have been developed to enable pervasive and real-time reporting of anomalies in cities (e.g., noise, illegal use of public facilities, urban infrastructure malfunctions). An interesting challenge in these applications is how to accurately predict an anomaly in a given region of the city before it happens. Prior works have made significant progress in anomaly detection. However, they can only detect anomalies after they happen, which may lead to significant information delay and lack of preparedness to handle the anomalies in an efficient way. In this paper, we develop a Crowdsourcing-based Urban Anomaly Prediction Scheme (CUAPS) to accurately predict the anomalies of a city by exploring both spatial and temporal information embedded in the crowdsourcing data. We evaluated the performance of our scheme and compared it to the state-of-the-art baselines using four real-world datasets collected from 311 service in the city of New York. The results showed that our scheme can predict different categories of anomalies in a city more accurately than the baselines.
Exploring personal knowledge extraction from conversations with charm
Authors: Anna Tigunova, Paramita Mirza, Andrew Yates, Gerhard Weikum
Venue: Conference WSDM 2021
Year: 2021

Incorporating users' personal facts enhances the quality of many downstream services. Automated extraction of such personal knowledge has recently received considerable attention. However, often the operation of extraction models is not exposed to the user, making predictions inexplicable. In this work we present a web demonstration platform showcasing a recent personal knowledge extraction model, CHARM, which provides information on how the prediction was made and which data was decisive for it. Our demonstration explores two potential sources of input data: conversational transcripts and social media submissions.