OpenRaiser/Intern-Atlas huggingface dataset:
Key opinion leaders in recommendation systems: Opinion elicitation and diffusion
Authors: Jianling Wang, Kaize Ding, Ziwei Zhu, Yin Zhang, James Caverlee
Venue: Conference WSDM 2020
Year: 2020

Recommendation systems typically rely on the interactions between a crowd of ordinary users and items, ignoring the fact that many real-world communities are notably influenced by a small group of key opinion leaders, whose feedback on items wields outsize influence. With important positions in the community (e.g. have a large number of followers), their elite opinions are able to diffuse to the community and further impact what items we buy, what media we consume, and how we interact with online platforms. Hence, this paper investigates how to develop a novel recommendation system by explicitly capturing the influence from key opinion leaders to the whole community. Centering around opinion elicitation and diffusion, we propose an end-to-end Graph-based neural model - GoRec. Specifically, to preserve the multi-relations between key opinion leaders and items, GoRec elicits the opinions from key opinion leaders with a translation-based embedding method. Moreover, GoRec adopts the idea of Graph Neural Networks to model the elite opinion diffusion process for improved recommendation. Through experiments on Goodreads and Epinions, the proposed model outperforms state-of-the-art approaches by 10.75% and 9.28% on average in Top-K item recommendation.
Predictive analysis by leveraging temporal user behavior and user embeddings
Authors: Charles Chen, Sungchul Kim, Hung Bui, Ryan A. Rossi, Eunyee Koh, Branislav Kveton, Razvan C. Bunescu
Venue: Conference CIKM 2018
Year: 2018

The rapid growth of mobile devices has resulted in the generation of a large number of user behavior logs that contain latent intentions and user interests. However, exploiting such data in real-world applications is still difficult for service providers due to the complexities of user behavior over a sheer number of possible actions that can vary according to time. In this work, a time-aware RNN model, TRNN, is proposed for predictive analysis from user behavior data. First, our approach predicts next user action more accurately than the baselines including the n-gram models as well as two recently introduced time-aware RNN approaches. Second, we use TRNN to learn user embeddings from sequences of user actions and show that overall the TRNN embeddings outperform conventional RNN embeddings. Similar to how word embeddings benefit a wide range of task in natural language processing, the learned user embeddings are general and could be used in a variety of tasks in the digital marketing area. This claim is supported empirically by evaluating their utility in user conversion prediction, and preferred application prediction. According to the evaluation results, TRNN embeddings perform better than the baselines including Bag of Words (BoW), TFIDF and Doc2Vec. We believe that TRNN embeddings provide an effective representation for solving practical tasks such as recommendation, user segmentation and predictive analysis of business metrics.
Worker types and personality traits in crowdsourcing relevance labels
Authors: Gabriella Kazai, Jaap Kamps, Natasa Milic-Frayling
Venue: Conference CIKM 2011
Year: 2011

Crowdsourcing platforms offer unprecedented opportunities for creating evaluation benchmarks, but suffer from varied output quality from crowd workers who possess different levels of competence and aspiration. This raises new challenges for quality control and requires an in-depth understanding of how workers' characteristics relate to the quality of their work.
Effects of User Negative Experience in Mobile News Streaming
Authors: Hongyu Lu, Min Zhang, Weizhi Ma, Ce Wang, Feng Xia, Yiqun Liu, Leyu Lin, Shaoping Ma
Venue: Conference SIGIR 2019
Year: 2019

Online news streaming services have been one of the major information acquisition resources for mobile users. In many cases, users click an article but find it cannot satisfy or even annoy them. Intuitively, these negative experiences will affect users' behaviors and satisfaction, but such effects have not been well understood. In this work, a retrospective analysis is conducted using real users' log data, containing user's explicit feedback of negative experiences, from a commercial news streaming application. Through multiple intra-session comparison experiments, we find that in current session, users will spend less time reading the content, lose activeness and leave sooner after having negative experiences. Later return and significant changes of user behaviors in the next session are also observed, which demonstrates the existence of inter-session effects of negative experiences. Since users' negative experiences are generally implicit, we further investigate the possibility and the approach to automatically identify them. Results show that using changes of both users' intra-session and inter-session behaviors achieves significant improvement. Besides the effects on user behaviors, we also explore the effects on user satisfaction by incorporating a laboratory user study. Results show that negative experiences reduce user satisfaction in the current session, and the impact will last to the next session. Moreover, we demonstrate users' negative feedback helps on the meta-evaluation of online metrics. Our research has comprehensively analyzed the impacts of users' item-level negative experiences, and shed light on the understanding of user behaviors and satisfaction.
Contextual spatial outlier detection with metric learning
Authors: Guanjie Zheng, Susan L. Brantley, Thomas Lauvaux, Zhenhui Li
Venue: Conference KDD 2017
Year: 2017

Hydraulic fracturing (or "fracking") is a revolutionary well stimulation technique for shale gas extraction, but has spawned controversy in environmental contamination. If methane from gas wells leaks extensively, this greenhouse gas can impact drinking water wells and enhance global warming. Our work is motivated by this heated debate on environmental issue and focuses on general data analytical techniques to detect anomalous spatial data samples (e.g., water samples related to potential leakages). Specifically, we propose a spatial outlier detection method based on contextual neighbors. Different from existing work, our approach utilizes both spatial attributes and non-spatial contextual attributes to define neighbors. We further use robust metric learning to combine different contextual attributes in order to find meaningful neighbors. Our technique can be applied to any spatial dataset. Extensive experimental results on five real-world datasets demonstrate the effectiveness of our approach. We also show some interesting case studies, including one case linking to leakage of a gas well.
Community mitigation: A data-driven system for covid-19 risk assessment in a hierarchical manner
Authors: Yanfang Ye, Yujie Fan, Shifu Hou, Yiming Zhang, Yiyue Qian, Shiyu Sun, Qian Peng, Mingxuan Ju, Wei Song, Kenneth A. Loparo
Venue: Conference CIKM 2020
Year: 2020

The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. To slow the spread of virus infections and better respond with actionable strategies for community mitigation, leveraging the large-scale and real-time pandemic related data generated from heterogeneous sources (e.g., disease related data, demographic data, mobility data, and social media data), in this work, we propose and develop a data-driven system (named α-satellite), as an initial offering, to provide real-time COVID-19 risk assessment in a hierarchical manner in the United States. More specifically, given a location (either user input or automatic positioning), the system will automatically provide risk indices associated with the specific location, the county that location is in and the state as a whole to enable people to select appropriate actions for protection while minimizing disruptions to daily life to the extent possible. In α-satellite, we first construct an attributed heterogeneous information network (AHIN) to model the collected multi-source data in a comprehensive way; and then we utilize meta-path based schemes to model both vertical and horizontal information associated with a given location (i.e., point of interest, POI); finally we devise a novel heterogeneous graph neural network to aggregate its neighborhood information to estimate the risk of the given POI in a hierarchical manner. To comprehensively evaluate the performance of α-satellite in real-time COVID-19 risk assessment, a set of studies are first performed to validate its utility; based on a real-world dataset consisting of 6,538 annotated POIs, the experimental results show that α-satellite achieves the area of under curve (AUC) of 0.9378, which outperforms the state-of-the-art baselines. After we launched the system for public tests, it had attracted 51,190 users as of May 30. Based on the analysis of its large-scale users, we have a key finding that people from more severe regions (i.e., with larger numbers of COVID-19 cases) have stronger interests using the system for actionable information. Our system and generated benchmark datasets have been made publicly accessible through our website.
Dynamic analysis of multiagent q–learning with ϵ–greedy exploration
Authors: Eduardo Rodrigues Gomes, Ryszard Kowalczyk
Venue: Conference ICML 2009
Year: 2009

The development of mechanisms to understand and model the expected behaviour of multiagent learners is becoming increasingly important as the area rapidly find application in a variety of domains. In this paper we present a framework to model the behaviour of Q-learning agents using the o-greedy exploration mechanism. For this, we analyse a continuous-time version of the Q-learning update rule and study how the presence of other agents and the o-greedy mechanism affect it. We then model the problem as a system of difference equations which is used to theoretically analyse the expected behaviour of the agents. The applicability of the framework is tested through experiments in typical games selected from the literature.
When does a camera see rain?
Authors: Kshitiz Garg, Shree K. Nayar
Venue: Conference ICCV 2005
Year: 2005

Rain produces sharp intensity fluctuations in images and videos, which degrade the performance of outdoor vision systems. These intensity fluctuations depend on various factors, such as the camera parameters, the properties of rain, and the brightness of the scene. We show that the properties of rain - its small drop size, high velocity, and low density - make its visibility strongly dependent on camera parameters such as exposure time and depth of field. We show that these parameters can be selected so as to reduce or even remove the effects of rain without altering the appearance of the scene. Conversely, the parameters of a camera can also be set to enhance the visual effects of rain. This can be used to develop an inexpensive and portable camera-based rain gauge that provides instantaneous rain rate measurements. The proposed methods serve to make vision algorithms more robust to rain without any necessity for post-processing. In addition, they can be used to control the visual effects of rain during the filming of movies
Neural formatting for spreadsheet tables
Authors: Haoyu Dong, Jinyu Wang, Zhouyu Fu, Shi Han, Dongmei Zhang
Venue: Conference CIKM 2020
Year: 2020

Spreadsheets are popular and widely used for data presentation and management, where users create tables in various structures to organize and present data. Table formatting is an important yet tedious task for better exhibiting table structures and data relationships. However, without the aid of intelligent tools, manual formatting remains a tedious and time-consuming task. In this paper, we propose CellGAN, a neural formatting model for learning and recommending formats of spreadsheet tables. Based on a novel conditional generative adversarial network (cGAN) architecture, CellGAN learns table formatting from real-world spreadsheet tables in a self-supervised fashion without requiring human labeling. In CellGAN we devise two mechanisms, row/column-wise pooling and local refinement network, to address challenges from the spreadsheet domain. We evaluate the effectiveness of CellGAN against real-world datasets using both quantitative metrics and human perception studies. The results indicate remarkable performance gains over rule-based methods, graphical models or direct application of the state-of-the-art cGANs used in visual synthesis tasks. Neural Formatting is the first step towards auto-formatting for spreadsheet tables with promising results.
Exploiting new sentiment-based meta-level features for effective sentiment analysis
Authors: Sérgio D. Canuto, Marcos André Gonçalves, Fabrício Benevenuto
Venue: Conference WSDM 2016
Year: 2016

In this paper we address the problem of automatically learning to classify the sentiment of short messages/reviews by exploiting information derived from meta-level features i.e., features derived primarily from the original bag-of-words representation. We propose new meta-level features especially designed for the sentiment analysis of short messages such as: (i) information derived from the sentiment distribution among the k nearest neighbors of a given short test document x, (ii) the distribution of distances of x to their neighbors and (iii) the document polarity of these neighbors given by unsupervised lexical-based methods. Our approach is also capable of exploiting information from the neighborhood of document x regarding (highly noisy) data obtained from 1.6 million Twitter messages with emoticons. The set of proposed features is capable of transforming the original feature space into a new one, potentially smaller and more informed. Experiments performed with a substantial number of datasets (nineteen) demonstrate that the effectiveness of the proposed sentiment-based meta-level features is not only superior to the traditional bag-of-word representation (by up to 16%) but is also superior in most cases to state-of-art meta-level features previously proposed in the literature for text classification tasks that do not take into account some idiosyncrasies of sentiment analysis. Our proposal is also largely superior to the best lexicon-based methods as well as to supervised combinations of them. In fact, the proposed approach is the only one to produce the best results in all tested datasets in all scenarios.
Multilabel classification with meta-level features
Authors: Siddharth Gopal, Yiming Yang
Venue: Conference SIGIR 2010
Year: 2010

Effective learning in multi-label classification (MLC) requires an appropriate level of abstraction for representing the relationship between each instance and multiple categories. Current MLC methods have been focused on learning-to-map from instances to ranked lists of categories in a relatively high-dimensional space. The fine-grained features in such a space may not be sufficiently expressive for characterizing discriminative patterns, and worse, make the model complexity unnecessarily high. This paper proposes an alternative approach by transforming conventional representations of instances and categories into a relatively small set of link-based meta-level features, and leveraging successful learning-to-rank retrieval algorithms (e.g., SVM-MAP) over this reduced feature space. Controlled experiments on multiple benchmark datasets show strong empirical evidence for the strength of the proposed approach, as it significantly outperformed several state-of-the-art methods, including Rank-SVM, ML-kNN and IBLR-ML (Instance-based Logistic Regression for Multi-label Classification) in most cases.
Rumor Detection with Field of Linear and Non-Linear Propagation
Authors: An Lao, Chongyang Shi, Yayi Yang
Venue: Conference WWW 2021
Year: 2021

The propagation of rumors is a complex and varied phenomenon. In the process of rumor dissemination, in addition to rumor claims, there will be abundant social context information surrounding the rumor. Therefore, it is vital to learn the characteristics of rumors in terms of both the linear temporal sequence and the non-linear diffusion structure simultaneously. However, in some existing research, time-dependent and diffusion-related information has not been fully utilized. Accordingly, in this paper, we propose a novel model Rumor Detection with Field of Linear and Non-Linear Propagation (RDLNP) to automatically detect rumors from the above two fields by taking advantage of claim content, social context and temporal information. First, the Rumor Hybrid Feature Learning (RHFL) we designed can extract the correlations between the claims and temporal information, differentiate the hybrid features of specific posts, and generate unified representations for rumors. Second, we proposed Non-Linear Structure Learning (NLSL) and Linear Sequence Learning (LSL) to integrate contextual features along the path of the diffusion structure and temporal engagement variation of responses respectively. Finally, Shared Feature Learning (SFL) models the representation reinforcement and learns the mutual influence between NLSL and LSL, and then highlights their valuable features. Experiments conduct on two public and widely used datasets, i.e. PHEME and RumorEval, demonstrate both the effectiveness and the outstanding performance of the proposed approach.
Monitorlight: Reinforcement learning-based traffic signal control using mixed pressure monitoring
Authors: Zekuan Fang, Fan Zhang, Ting Wang, Xiang Lian, Mingsong Chen
Venue: Conference CIKM 2022
Year: 2022

Although Reinforcement Learning (RL) has achieved significant success in the Traffic Signal Control (TSC), most of them focus on the design of RL elements while the impact of the phase duration is neglected. Due to the lack of exploring dynamic phase duration, the overall performance and convergence rate of RL-based TSC approaches cannot be guaranteed, which may result in poor adaptability of RL methods to different traffic conditions. To address these issues, in this paper, we formulate a novel phase-duration-aware TSC (PDA-TSC) problem and propose an effective RL-based TSC approach, named MonitorLight. Our approach adopts a new traffic indicator, mixed pressure, which enables RL agents to simultaneously analyze the impacts of stationary and moving vehicles on intersections. Based on the observed mixed pressure of intersections, RL agents can autonomously determine whether or not to change the current signals in real-time. In addition, MonitorLight can adjust the control method for scenarios with different real-time requirements and achieve excellent results in different situations. Extensive experiments on both real-world and synthetic datasets demonstrate that MonitorLight outperforms the current state-of-the-art IPDALight by up to 2.84% and 5.71% in average vehicle travel time, respectively. Moreover, our method significantly speeds up the convergence, leading IPDALight by 36.87% and 34.58% in the start to converge episode and jumpstart performance, respectively.
Time critic policy gradient methods for traffic signal control in complex and congested scenarios
Authors: Stefano Giovanni Rizzo, Giovanna Vantini, Sanjay Chawla
Venue: Conference KDD 2019
Year: 2019

Employing an optimal traffic light control policy has the potential of having a positive impact, both economic and environmental, on urban mobility. Reinforcement learning techniques have shown promising results in optimizing control policies for basic intersections and low volume traffic. This paper addresses the traffic light control problem in a complex scenario, such as a signalized roundabout with heavy traffic volumes, with the aim of maximizing throughput and avoiding traffic jams. We formulate the environment with a realistic representation of states and actions and a capacity-based reward. We enforce episode terminal conditions to avoid unwanted states, such as long queues interfering with other junctions in the vehicular network. A time-dependent baseline is proposed to reduce the variance of Policy Gradient updates in the setting of episodic conditions, thus improving the algorithm convergence to an optimal solution. We evaluate the method on real data and highly congested traffic, implementing a signalized simulated roundabout with 11 phases. The proposed method is able to avoid traffic jams and achieves higher performance than traditional time-splitting policies and standard Policy Gradient on average delay and effective capacity, while drastically decreasing the emissions.
FUNNEL: automatic mining of spatially coevolving epidemics
Authors: Yasuko Matsubara, Yasushi Sakurai, Willem G. van Panhuis, Christos Faloutsos
Venue: Conference KDD 2014
Year: 2014

Given a large collection of epidemiological data consisting of the count of d contagious diseases for l locations of duration n, how can we find patterns, rules and outliers? For example, the Project Tycho provides open access to the count infections for U.S. states from 1888 to 2013, for 56 contagious diseases (e.g., measles, influenza), which include missing values, possible recording errors, sudden spikes (or dives) of infections, etc. So how can we find a combined model, for all these diseases, locations, and time-ticks? In this paper, we present FUNNEL, a unifying analytical model for large scale epidemiological data, as well as a novel fitting algorithm, FUNNELFIT, which solves the above problem. Our method has the following properties: (a) Sense-making: it detects important patterns of epidemics, such as periodicities, the appearance of vaccines, external shock events, and more; (b) Parameter-free: our modeling framework frees the user from providing parameter values; (c) Scalable: FUNNELFIT is carefully designed to be linear on the input size; (d) General: our model is general and practical, which can be applied to various types of epidemics, including computer-virus propagation, as well as human diseases. Extensive experiments on real data demonstrate that FUNNELFIT does indeed discover important properties of epidemics: (P1) disease seasonality, e.g., influenza spikes in January, Lyme disease spikes in July and the absence of yearly periodicity for gonorrhea; (P2) disease reduction effect, e.g., the appearance of vaccines; (P3) local/state-level sensitivity, e.g., many measles cases in NY; (P4) external shock events, e.g., historical flu pandemics; (P5) detect incongruous values, i.e., data reporting errors.
A framework for safely publishing communication traces
Authors: Abhinav Parate, Gerome Miklau
Venue: Conference CIKM 2009
Year: 2009

A communication trace is a detailed record of the communication between two entities. Communication traces are vital for research in computer networks and protocols in many domains, but their release is severely constrained by privacy and security concerns. In this paper, we propose a framework in which a trace owner can match an anonymizing transformation with the requirements of analysts. The trace owner can release multiple transformed traces, each customized to an analyst’s needs, or a single transformation satisfying all requirements. The framework enables formal reasoning about anonymization policies, for example to verify that a given trace has utility for the analyst, or to obtain the most secure anonymization for the desired level of utility. Because communication traces are typically very large, we also provide techniques that allow efficient application of transformations using relational database systems. 1.