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Event Classification in Microblogs via Social Tracking

Social media websites have become important information sharing platforms. The rapid development of social media platforms has led to increasingly... (more)

Exploiting Social-Mobile Information for Location Visualization

With a smart phone at hand, it becomes easy now to snap pictures and publish them online with few lines of texts. The GPS coordinates and... (more)

Directly Optimize Diversity Evaluation Measures

The queries issued to search engines are often ambiguous or multifaceted, which requires search engines to return diverse results that can fulfill as many different information needs as possible; this is called search result diversification. Recently, the relational learning to rank model, which designs a learnable ranking function following the... (more)

Nonnegative Matrix Factorization with Integrated Graph and Feature Learning

Matrix factorization is a useful technique for data representation in many data mining and machine learning tasks. Particularly, for data sets with... (more)

Learning k for kNN Classification

The K Nearest Neighbor (kNN) method has widely been used in the applications of data mining and machine learning due to its simple implementation and distinguished performance. However, setting all test data with the same k value in the previous kNN methods has been proven to make these methods impractical in real applications. This article... (more)

A Distribution Separation Method Using Irrelevance Feedback Data for Information Retrieval

In many research and application areas, such as information retrieval and machine learning, we often encounter dealing with a probability distribution... (more)

Daehr

Electronic health records (EHR) provide a rich source of temporal data that present a unique opportunity to characterize disease patterns and risk of imminent disease. While many data-mining tools have been adopted for EHR-based disease early detection, linear discriminant analysis (LDA) is one of the most commonly used statistical methods.... (more)

NEWS

Recent TIST News: 

ACM Transactions on Intelligent Systems and Technology (TIST) has been a success story.  Submissions to the journal have increase 76 percent from 2013 to 2015, from 278 original papers and revisions to 488.  Despite this increase, the journal acceptance rate has remained at a steady rate of approximately 24 percent. Furthermore, the TIST Impact Factor increased from 1.251 in 2014 to 2.414 in 2015.  


Journal Metric

  • - Impact Factor: 2.4
  • - 5-year Impact Factor: 9.15

About TIST

ACM Transactions on Intelligent Systems and Technology (ACM TIST) is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. READ MORE

Forthcoming Articles
Exploring Communication Behaviors of Users to Target PotentialUsers in Mobile Social Networks

In mobile social networks, users can communicate with each other over different telecom carriers. Thus, for telecom operators, how to acquire and retain users is a significant issue. The work of churn prediction is to determine whether a customer would leave soon. Differing from churn prediction, our work is to find those users who are likely to join target services from the competitors in the near future, where these users are called potential users. To target potential users, we propose a framework including feature extraction, feature selection, and classifier learning to solve the problem. First, we construct a heterogeneous information network from the call detail records of users. Then, we extract the explicit features from potential users interaction behavior in the heterogeneous information network. Moreover, because users are influenced by their community, we extract implicit features of potential users. After feature extraction, we explore the Information Gain to select the effective features. We use the effective explicit and implicit features to learn potential user classifiers, and use the classifiers to determine the potential users. Finally, we conduct experiments on real datasets. The results of our experiments show that the features extracted by our proposed method can be effective for targeting potential users.

Mobile Social Multimedia Analytics in the Big Data Era: An Introduction to the Special Issue

Securely Computing a Ground Speed Model

Consider a server offering risk assessment services, and potential clients of these services. The risk assessment model that is run by the server is based on current and historical data of the clients. However, the clients might prefer not sharing such sensitive data with external parties such as the server, and the server itself might consider the possession of this data as a liability rather than an asset. Secure multi-party computation (MPC) enables in principle to compute any function while hiding the inputs to the function, and would thus enable the computation of the risk assessment model while hiding the clients data from the server. However, a direct application of a generic MPC solution to this problem is rather inefficient, due to the large scale of the data and the complexity of the function. We describe a very efficient secure computation solution that is tailored for this problem. This solution demonstrates that a risk model can be applied over encrypted data fast enough to fit the requirements of commercial systems.

Automatic Construction of Statechart-Based Anomaly Detection Models for Multi-Threaded Industrial Control Systems

Traffic of Industrial Control System (ICS) between the Human Machine Interface (HMI) and the Programmable Logic Controller (PLC) is known to be highly periodic. However, it is sometimes multiplexed, due to asynchronous scheduling. Modeling the network traffic patterns of multiplexed ICS streams using Deterministic Finite Automata (DFA) for anomaly detection typically produces a very large DFA, and a high false-alarm rate. In this paper we introduce a new modeling approach that addresses this gap. Our Statechart DFA modeling includes multiple DFAs, one per cyclic pattern, together with a DFA-selector that de-multiplexes the incoming traffic into sub-channels and sends them to their respective DFAs. We demonstrate how to automatically construct the statechart from a captured traffic stream. Our learning algorithms first build a Discrete-Time Markov Chain (DTMC) from the stream. Next we split the symbols into sets, one per multiplexed cycle, based on symbol frequencies and node degrees in the DTMC graph. Then we create a sub-graph for each cycle, and extract Euler cycles for each sub-graph. The final statechart is comprised of one DFA per Euler cycle. The algorithms allow for non-unique symbols, that appear in more than one cycle, and also for symbols that appear more than once in a cycle. We evaluated our solution on traces from a production ICS using the Siemens S7-0x72 protocol. We also stress-tested our algorithms on a collection of synthetically-generated traces that simulated multiplexed ICS traces with varying levels of symbol uniqueness and time overlap. The algorithms were able to split the symbols into sets with 99.6% accuracy. The resulting statechart modeled the traces with a false-alarm rate as low as 2.27% in all but the more severe cases and 4.3% overall. In all but the most extreme scenarios the {\em Statechart} model drastically reduced both the false-alarm rate and the learned model size in comparison with the naive single-DFA model.

Tracking Illicit Drug Dealing and Abuse on Instagram using Multimodal Analysis

Illicit drug trade via social media sites, especially photo-oriented Instagram, has become a severe problem in recent years. As a result, tracking drug dealing and abuse on Instagram is of interest to law enforcement agencies and public health agencies. However, traditional approaches are based on manual search and browsing by trained domain experts, which suffer from the problem of poor scalability and reproducibility. In this paper, we propose a novel approach to detecting drug abuse and dealing automatically by utilizing multimodal data on social media. This approach also enables us to identify drug-related posts and analyze the behavior patterns of drug-related user accounts. To better utilize multimodal data on social media, multimodal analysis methods including multi-task learning and decision-level fusion are employed in our framework. We collect three datasets using Instagram and web search engine for training and testing our models. Experiment results on expertly labeled data have demonstrated the effectiveness of our approach, as well as its scalability and reproducibility over labor-intensive conventional approaches.

Advanced Economic Control of Electricity-based Space Heating Systems in Domestic Coalitions with Shared Intermittent Energy Resources

Over the past few years, domestic heating automation systems (DHASs) that optimize the domestic space heating control process with minimum user-input, utilizing appropriate occupancy prediction technology, have emerged as commercial products (e.g, the smart thermostats from Nest and Honeywell). At the same time, many houses are being equipped with, potentially grid-connected, intermittent energy resources (IERs), such as rooftop photovoltaic systems and/or small wind turbine generators. Now, in many regions of the world, such houses can sell energy to the grid but at a lower price than the price of buying it. In this context, and given the anticipated increase in electrification of heating, the next generation DHASs need to incorporate advanced economic control (AEC). Such AEC can exploit the energy buffer that heating loads provide, in order to shift the consumption of electricity-based heating systems to follow the intermittent energy generation of the house. By so doing, the energy imported from the grid can be minimized and considerable monetary gains for the household can be achieved, without affecting the occupants' schedule. These benefits can be amplified still further in domestic coalitions, where a number of houses come together and share their IER generation to minimize their cumulative grid energy import. Given the above, in this work we extend a state-of-the-art DHAS, to propose AdaHeat+, a practical DHAS, that, for the first time, incorporates AEC. Our work is applicable to both individual houses and domestic coalitions and comes complete with a cost allocation mechanism to share the gains of the coalition. Importantly, we propose an effective heuristic heating schedule planning approach for collective AEC which: (i) has a complexity that scales in a linear and parallelizable manner with the size of the coalition, and (ii) enables AdaHeat+ to handle different preferences, in balancing heating cost and thermal discomfort of the individual households. Our approach relies on stochastic IER power output predictions. To achieve this, we propose a new adaptive site-specific calibration technique to improve such predictions, utilizing Gaussian process modeling. Finally, we demonstrate the effectiveness of AdaHeat+ through real data evaluation, to show that collective AEC can improve heating cost-efficiency by up to 60%, compared to independent AEC (and even more when compared to no-AEC).

Data-Driven Frequency-Based Airline Profit Maximization

Though there are numerous traditional models to predict market share and demand along airline routes, the prediction of existing models is not precise enough and, to the best of our knowledge, there is no use of data-mining based forecasting techniques to improve airline profitability. We propose the MAP (Maximizing Airline Profits) architecture designed to help airlines and make two key contributions in airline market share and route demand prediction and prediction-based airline profit optimization. Compared with past methods to forecast market share and demand along airline routes, we introduce a novel Ensemble Forecasting (MAP-EF) approach considering two new classes of features: (i) features derived from clusters of similar routes, and (ii) features based on equilibrium pricing. We show that MAP-EF achieves much better Pearson Correlation Coefficients (over 0.95 vs. 0.82 for market share, 0.98 vs. 0.77 for demand) and R2-values compared with three state-of-the-art works for forecasting market share and demand, while showing much lower variance. Using the results of MAP-EF, we develop MAP-Bilevel Branch and Bound (MAP-BBB) and MAP-Greedy (MAP-G) algorithms to optimally allocate flight frequencies over multiple routes, to maximize an airlines profit. We also study two extensions of the profit maximization problem considering frequency constraints and long term profits. Furthermore, we develop algorithms for computing Nash equilibrium frequencies when there are multiple strategic airlines. Experimental results show that airlines can increase profits by a significant margin. All experiments were conducted with data aggregated from four sources: US Bureau of Transportation Statistics (BTS), US Bureau of Economic Analysis (BEA), the National Transportation Safety Board (NTSB), and the US Census Bureau (CB).

Modeling Topics and Behaviors of Microbloggers: An Integrated Approach

Microblogging encompasses both user generated content and behaviors. Microblogging users' behaviors include adoption specific hashtags, retweeting specific incoming tweets, etc.. When modeling microblogging data, one has to consider personal and background topics, as well as how these topics generate the observed content and behaviors. In this paper, we propose the Generalized Behavior-Topic (GBT) model for simultaneously modeling background topics and users' topical interest in microblogging data. GBT considers multiple topical communities (or realms) with different background topical interests while learning the personal topics of each user and her dependence on realms to generate both content and behavior. This differentiates GBT from other previous works that consider either one realm only or content data only. By associating user behaviors with the latent background and personal topics, GBT helps to model the user behaviors by the two types of topics. GBT also distinguishes itself from other earlier ones by modeling multiple types of behaviors together. Our experiments on two Twitter datasets show that GBT can effectively mine the representative topics for each realm. We also demonstrate that GBT significantly outperforms other state-of-the-art models in modeling content topics and user profiling.

CRADLE: An Online Plan Recognition Algorithm for Exploratory Domains

activities, extraneous actions, and mistakes. Such settings are prevalent in real world applications such as interaction with open-ended software, collaborative office assistants, and integrated development environments. Despite the prevalence of such settings in the real world, there is scarce work in formalizing the connection between high-level goals and low-level behavior and inferring the former from the latter in these settings. We present a formal grammar for describing users activities in such domains. We describe a new top-down plan recognition algorithm called CRADLE that uses this grammar to recognize agents interactions in exploratory domains. We compare the performance of CRADLE with state-of-the-art plan recognition algorithms in several experimental settings consisting of real and simulated data. Our results show that CRADLE was able to output plans exponentially more quickly than the state-of-the-art without compromising its correctness, as determined by domain experts. Our approach can form the basis of future systems that use plan recognition to provide real-time support to users in a growing class of interesting and challenging domains.

Location-Based Parallel Tag Completion for Geo-tagged Social Image Retrieval

Benefit from tremendous growth of user-generated content, social annotated tags get higher importance in organization and retrieval of large scale image database on Online Sharing Websites (OSW). To obtain high-quality tags from existing community contributed tags with missing information and noise, tag-based annotation or recommendation methods have been proposed for performance promotion of tag prediction. While images from OSW contain rich social attributes, existing studies only utilize the relations between visual content and tags to construct global information completion models. In this paper, beyond the image-tag relation, we take full advantage of the ubiquitous GPS locations and image-user relationship, to enhance the accuracy of tag prediction and improve the computational efficiency. For GPS locations, we define the popular geo-locations where people tend to take more images as Points of Interests (POI), which are discovered by mean shift approach. For image-user relationship, we integrate a localized prior constraint, expecting the completed tag sub-matrix in each POI to maintain consistency with users tagging behaviors. Based on these two key issues, we propose a unified tag matrix completion framework which learns the image-tag relation within each POI. To solve the proposed model, an efficient proximal sub-gradient descent algorithm is designed. The model optimization can be easily parallelized and distributed to learn the tag sub-matrix for each POI. Extensive experimental results reveal that the learned tag sub-matrix of each POI reflects the major trend of users tagging results with respect to different POIs and users, and the parallel learning process provides strong support for processing large scale online image database. To fit the response time requirement and storage limitations of tag-based image retrieval (TBIR) on mobile devices, we introduce Asymmetric Locality Sensitive Hashing (ALSH) to reduce the time cost and meanwhile improve the efficiency of retrieval.

Cost-Optimized Microblog Distribution over Geo-Distributed Data Centers: Insights from Cross-Media Analysis

The unprecedent growth of microblog services poses significant challenges on network traffic and service latency to the underlay infrastructure (i.e., geo-distributed data centers). Furthermore, the dynamic evolution in microblog status generates a huge workload on data consistence maintenance. In this paper, motivated by insights of cross media analysis based propagation patterns, we propose a novel cache strategy for microblog service systems to reduce the inter data center traffic and consistence maintenance cost, while achieve low service latency. Specifically, we first present a microblog classification method, which utilizes the external knowledge from correlated domains, to categorize microblogs. Then we conduct a large-scale measurement on a representative online social network system to study the category based propagation diversity on region and time scales. These insights illustrate social common habits on creating and consuming microblogs, and further motivate our architecture design. Finally, we formulate the content cache problem as a constrained optimization problem. By jointly using the Lyapunov optimization framework and simplex gradient method, we find the optimal online control strategy. Extensive trace driven experiments further demonstrate that our algorithm reduces the system cost by 24.5\% against traditional approaches with the same service latency.

An Unsupervised Approach to Inferring the Localness of People Using Incomplete Geo-Temporal Online Check-in Data

Inferring the localness of people is to identify whether a person is a local resident in a city or not by analyzing online check-in points that are contributed by users consisting of both local and non-local people (e.g., tourists). This information is critical for the targeted ads of local business, urban planning, and localized news recommendations. While there are prior work on geo-locating people in a city using supervised learning approaches, the accuracy of those techniques largely depends on the training datasets with complete geo-temporal information, which are difficult and expensive to obtain in practice. In this paper, we propose an unsupervised approach to infer the localness of people in a city by using the incomplete crowdsourcing data (i.e., online check-in points) that are publicly available. In particular, we develop an Incomplete-Geo-Temporal Expectation Maximization (IGT-EM) scheme, which incorporates a set of hidden variables to represent the localness of people and a set of estimation parameters to represent the likelihood of venues to attract local and non-local people respectively. Our solution can jointly estimate 1) the localness of a person and 2) the probability of a venue to attract local people without requiring any training data. We also implement a parallel IGT-EM algorithm by leveraging the computing power of a Graphic Processing Unit (GPU) that consists of 2496 cores. We evaluate our new approach on four real-world datasets collected from the city of New York, Chicago, Boston and Washington D.C. The results showed that our approach can accurately estimate the localness of people and significantly outperform other state-of-the-art baselines in terms of both estimation accuracy and execution time.

i2tag: RFID Mobility and Activity Identification through Intelligent Profiling

Many Radio Frequency Identification (RFID) applications, e.g., virtual shopping-cart and tag-assisted gaming, involve sensing and recognizing tag mobility. Existing RFID localization techniques however are mostly designed for static or slowly moving targets (less than 0.3 m/s). More importantly, we observe that prior schemes suffer from serious performance degradation for detecting realworld moving tags in typical indoor environments with multipath interference. In this paper, we present i2tag, an intelligent mobility-aware activity identification system for RFID tags in multipath-rich environments, e.g., indoors. i2tag employs a supervised learning framework based on our novel fine-grained mobility profile, which can quantify different levels of mobility. Unlike previous methods that mostly rely on phase measurement, i2tag takes into account various measurements, including RSSI variance, packet loss rate, and our novel relative-phase-based fingerprint. Additionally, we design a multiple dimensional dynamic time warping based algorithm to robustly detect mobility and the associated activities. We show that i2tag is readily deployable using off-the-shelf RFID devices. A prototype has been implemented using a Thingmagic reader and standard-compatible tags. Experimental results demonstrate its superiority in mobility detection and activity identification in various indoor environments.

ST-SAGE: A Spatial-Temporal Sparse Additive Generative Model for Spatial Item Recommendation

With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important mobile application, especially when users travel away from home. However, this type of recommendation is very challenging compared to traditional recommender systems. A user may visit only a limited number of spatial items, leading to a very sparse user-item matrix. This matrix becomes even sparser when the user travels to a distant place as most of the items visited by a user are usually located within a short distance from the user's home. Moreover, user interests and behavior patterns may vary dramatically across different time and different geographical regions. In light of this, we propose ST-SAGE, a spatial-temporal sparse additive generative model for spatial item recommendation in this paper. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns of spatial items and the content of spatial items. To further alleviate the data sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd's preferences over a well-designed spatial index structure called spatial pyramid. To speed up the training process of ST-SAGE, we implement a parallel version of the model inference algorithm on the GraphLab framework. We conduct extensive experiments and the experimental results clearly demonstrate that ST-SAGE outperforms the state-of-the-art recommender systems in terms of recommendation effectiveness, model training efficiency and online recommendation efficiency.

Bibliometrics

Publication Years 2010-2017
Publication Count 440
Citation Count 5163
Available for Download 440
Downloads (6 weeks) 4564
Downloads (12 Months) 44741
Downloads (cumulative) 197554
Average downloads per article 449
Average citations per article 12
First Name Last Name Award
Benjamin B Bederson ACM Distinguished Member (2011)
Andrei Broder ACM Paris Kanellakis Theory and Practice Award (2012)
Carlos A. Castillo ACM Senior Member (2014)
Charles L A Clarke ACM Distinguished Member (2015)
Ingemar J. Cox ACM Distinguished Member (2011)
Alberto Del Bimbo ACM Distinguished Member (2016)
Deborah Estrin ACM Athena Lecturer Award (2006)
Maria L Gini ACM Distinguished Member (2006)
Xian-Sheng Hua ACM Distinguished Member (2015)
ACM Senior Member (2009)
Chih-Jen Lin ACM Distinguished Member (2011)
ACM Senior Member (2010)
C.L. Liu ACM Karl V. Karlstrom Outstanding Educator Award (1989)
Tao Mei ACM Distinguished Member (2016)
ACM Senior Member (2012)
Jeffrey Nichols ACM Senior Member (2013)
Judea Pearl ACM A. M. Turing Award (2011)
ACM AAAI Allen Newell Award (2003)
Jian Pei ACM Senior Member (2007)
Yong Rui ACM Distinguished Member (2009)
ACM Senior Member (2006)
Stefan Savage ACM Prize in Computing (2015)
Yoav Shoham ACM AAAI Allen Newell Award (2012)
Gita Reese Sukthankar ACM Senior Member (2013)
Jaime Teevan ACM Senior Member (2012)
Moshe Tennenholtz ACM AAAI Allen Newell Award (2012)
Feiyue Wang ACM Distinguished Member (2007)
Xing Xie ACM Senior Member (2010)
Hui Xiong ACM Distinguished Member (2014)
ACM Senior Member (2010)
Shuicheng Yan ACM Distinguished Member (2016)
Qiang Yang ACM Distinguished Member (2011)
Franco Zambonelli ACM Distinguished Member (2012)
ACM Senior Member (2009)
Yu Zheng ACM Distinguished Member (2016)
ACM Senior Member (2011)
Michelle Zhou ACM Distinguished Member (2009)
ACM Senior Member (2007)
Michelle Zhou ACM Distinguished Member (2009)
ACM Senior Member (2007)

First Name Last Name Paper Counts
Dacheng Tao 7
Xing Xie 7
Enhong Chen 6
Tatseng Chua 5
Nicholasjing Yuan 5
Yu Zheng 5
Xiansheng HUA 5
Jinhui Tang 5
Shuicheng Yan 5
Steven Hoi 4
Xuan Song 4
Ryosuke Shibasaki 4
Changsheng Xu 4
Qiang Yang 4
Michelle Zhou 4
Quanshi Zhang 3
Philip YU 3
Martha Larson 3
Christopherchuen Yang 3
Wen Gao 3
Xue Li 3
Hui Xiong 3
Rongrong Ji 3
Xiaowei Shao 3
Huanhuan Cao 3
Francesco Bonchi 3
Irwin King 3
Rebecca Castaño 3
Qi Tian 3
Wenchih Peng 3
Tao Li 3
Shuaiqiang Wang 2
Qingzhong Liu 2
Jiawei Han 2
Luan Tang 2
Jilei Tian 2
Mahdi Jalili 2
Claudio Biancalana 2
Giuseppe Sansonetti 2
Anlei Dong 2
Chong Peng 2
Luca Cagliero 2
Yue Shi 2
Alan Hanjalic 2
Charles Ling 2
Daqing Zhang 2
Jure Leskovec 2
Mohan Kankanhalli 2
Zhengjun Zha 2
Yue Gao 2
Yuval Elovici 2
Yoshinobu Kawahara 2
Chihjen Lin 2
Diane Cook 2
Defu Lian 2
Elena Baralis 2
Tania Cerquitelli 2
Robin Cohen 2
SungWook Yoon 2
Jie Cheng 2
Mahmud Hossain 2
Quan Fang 2
Vincent Tseng 2
Sihong Xie 2
Hongxun Yao 2
Zhiwen Yu 2
Paulo Shakarian 2
Hongyuan Zha 2
Haggai Roitman 2
Liyan Zhang 2
Alex Rogers 2
Alberto Del Bimbo 2
Yongdong Zhang 2
Amin Javari 2
Jian Pei 2
Xueqi Cheng 2
Alexander Artikis 2
Venkatramanan Subrahmanian 2
Maria Sapino 2
Guirong Xue 2
Iván Cantador 2
Ido Guy 2
Bohao Chen 2
Yixin Chen 2
Fuzheng Zhang 2
Nathan Eagle 2
Manish Marwah 2
Hanqing Lu 2
Tao Mei 2
Pablo Castells 2
Meir Kalech 2
Daxin Jiang 2
Xuning Tang 2
Katia Sycara 2
Rino Falcone 2
Jinshi Cui 2
Jia Zeng 2
Dana Nau 2
Shoude Lin 2
Ling Guan 2
Michael Fire 2
Laiwan Chan 2
Neil Yorke-Smith 2
Meng Wang 2
Jaegil Lee 2
Ratnesh Sharma 2
Fabio Gasparetti 2
Alessandro Micarelli 2
Munindar Singh 2
Gita Sukthankar 2
Zhiyuan Cheng 2
John Dickerson 2
Alvin Chin 2
David Carmel 2
Michael Lyu 2
Vito Ostuni 2
Jun Ma 2
Jiuyong Li 2
Yuichi Motai 2
Masaki Aono 2
Bingbing Ni 2
David Thompson 2
Yihsuan Yang 2
Benno Stein 2
Alejandro Bellogín 2
Jeffrey Nichols 2
John Doucette 2
Daqing Zhang 2
Tommaso Noia 2
Zhi Geng 2
Kun Zhang 2
Bernhard Schölkopf 2
Ramesh Jain 2
Naren Ramakrishnan 2
Sarit Kraus 2
Jitao Sang 2
Lior Rokach 2
Kiri Wagstaff 2
Martin Potthast 2
Alan Said 2
Li Chen 2
Rui Zhang 2
Eugenio Sciascio 2
Xavier Serra 2
Shihchia Huang 2
Huijing Zhao 2
Xindong Wu 2
Shulamit Reches 2
Wangchien Lee 2
Subbarao Kambhampati 2
Jamal Bentahar 2
Kyumin Lee 2
James Caverlee 2
Thomas Dietterich 2
Jalal Mahmud 2
Robin Cohen 2
Ya'akov Gal 2
Lars Karlsson 1
Jianhua Guo 1
Amy Fire 1
Xiaogang Dong 1
Peng Ding 1
Jiji Zhang 1
Thucduy Le 1
Nicholas Jennings 1
Weisheng Chin 1
Yong Zhuang 1
Zhao Zhang 1
Chenglin Liu 1
Rong Jin 1
Jameson Toole 1
Andreas Krause 1
Perukrishnen Vytelingum 1
Nicholas Jennings 1
Pauline Berry 1
Mitchell Ai-Chang 1
Juan Castilla-Rubio 1
Wei Ding 1
Wei Chen 1
RubéN Lara 1
Dell Zhang 1
Edleno Moura 1
Erik Saule 1
Howard Tennen 1
Aaron Steele 1
Sukjin Lee 1
Yubin Park 1
Pasquale Lops 1
Christopher Yang 1
Simon Pool 1
Juanzi Li 1
Emil Stefanov 1
Elaine Shi 1
Dan Ventura 1
Saranya Krishnamoorthy 1
Yunhong Hu 1
Amit Chopra 1
Munindar Singh 1
Matteo Venanzi 1
Mohamed Daoudi 1
Liangtien Chia 1
Timothy Shih 1
Yo Ehara 1
Khoi Nguyen 1
Feiyue Wang 1
Xiaolong Zhang 1
Zhanyi Liu 1
Wentao Zheng 1
Andrei Broder 1
Chengbo Zhang 1
Luheng He 1
Min Zhao 1
Enrique Chavarriaga 1
Bo Han 1
Shaodian Zhang 1
Kathleen Carley 1
Hai Yang 1
Eric Lu 1
Qi Liu 1
Hsunping Hsieh 1
Chengte Li 1
Gavin McArdle 1
Isamu Okada 1
Gerhard Widmer 1
Ke Chen 1
Markus Schedl 1
Giuseppe Manco 1
Andreas Rauber 1
Qiang Cheng 1
Pakkin Wong 1
Ruoyun Huang 1
Zhihua Zhou 1
Emilio Ferrara 1
Geert Houben 1
Neil Rubens 1
Thomas Porta 1
Myungcheol Doo 1
Ling Liu 1
Wei Gao 1
François Poulet 1
Federico Chesani 1
Luigi Grimaudo 1
Jianhui Ye 1
Anshul Sawant 1
Mohammadtaghi Hajiaghayi 1
Hiroyuki Yoshida 1
Valeria Soto-Mendoza 1
Jesús Favela 1
Maythe Rojas 1
Yunchao Wei 1
Guangchan Liu 1
Robert Morris 1
Mohammad Hossain 1
Ghulam Muhammad 1
Prithviraj Dasgupta 1
Sarah Schulz 1
Xiaoming Zhang 1
Jasminko Novak 1
Mark Melenhorst 1
Dimitrios Michalopoulos 1
Huaming Rao 1
Michael Borish 1
Advaith Siddharthan 1
Richard Comont 1
Elaine O'mahony 1
Krzysztof Grudzien 1
Pengfei Xu 1
Xiaoshuai Sun 1
Shengping Zhang 1
Julian Panetta 1
Daqing Zhang 1
Karl Aberer 1
Lingyin Wei 1
Thomas Springer 1
Hiroshi Motoda 1
Haifeng Wang 1
Taesun Moon 1
Houda Bouamor 1
Arthur Asuncion 1
Kasim Candan 1
Xuegang Hu 1
Christos Anagnostopoulos 1
Bonnie Dorr 1
Hong Zhou 1
Steven Skiena 1
Bernhard Pfahringer 1
Jie Tang 1
Philippe De Wilde 1
Jiaul Paik 1
Rodrygo Santos 1
Gerhard Tröster, 1
Zhifeng Li 1
Qi Liu 1
Daniel Gatica-Perez 1
Markus Strohmaier 1
Dominik Benz 1
Elad Yom-Tov 1
Ethan Trewhitt 1
Chongjie Zhang 1
Phillip DiBona 1
Martin Hofmann 1
Haofen Wang 1
Szuhao Huang 1
Shanghong Lai 1
Vivekanand Gopalkrishnan 1
Thomas Tran 1
Zhiyuan Liu 1
Marina Spivak 1
Lawrence Saul 1
Evangelos Milios 1
Inderjit Dhillon 1
Hang Li 1
Vanja Josifovski 1
Lance Riedel 1
Weishi Zhang 1
Yi Chang 1
Li Song 1
Yiliang Zhao 1
Wei Jiang 1
Ali Hurson 1
Francisco Rodríguez-Serrano 1
Nicola Barbieri 1
Sungsu Lim 1
Ubai Sandouk 1
Mark Sandler 1
Yan Liu 1
Shenghua Zhong 1
W Towne 1
Changshing Perng 1
Sushil Jajodia 1
Jing Lv 1
Xiaofang Zhou 1
William Groves 1
Frederic Font 1
Fanchieh Cheng 1
Fabrizio Silvestri 1
Yinting Wang 1
Tao Guan 1
Liya Duan 1
Chongyu Chen 1
Meng Wang 1
Haiyan Li 1
Nan Dong 1
Guodong Guo 1
Haiwei Dong 1
Hong Liu 1
Kartik Talamadupula 1
Alessandro Saffiotti 1
Daniel Neill 1
Jiadong Zhang 1
Beryl Plimmer 1
Steven Reece 1
Hoda Sepehri Rad 1
Vincentwenchen Zheng 1
Andrew Sung 1
Mengyu Qiao 1
Jiebo Luo 1
Roland Kays 1
Karen Haigh 1
Joshua Plotkin 1
Shyam Boriah 1
Christopher Potter 1
Zhenyu Lu 1
Rómer Rosales 1
Si Liu 1
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Affiliation Paper Counts
Amazon.com, Inc. 1
Nanyang Technological University School of Computer Engineering 1
Florida Institute for Human & Machine Cognition 1
Fujitsu America, Inc. 1
Shandong University of Finance 1
Shandong Academy of Sciences 1
Austrian Institute of Technology 1
Laboratoire d'Informatique de Nantes-Atlantique 1
Yuncheng University 1
Liverpool Hope University 1
Qatar Foundation 1
Polytechnic School of Montreal 1
Netherlands Organisation for Applied Scientific Research - TNO 1
Capital Normal University China 1
Binghamton University State University of New York 1
American University 1
Massachusetts General Hospital and Harvard Medical School 1
University of Surrey 1
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ACM Transactions on Intelligent Systems and Technology (TIST)
Archive


2017
Volume 8 Issue 3, February 2017  Issue-in-Progress
Volume 8 Issue 2, January 2017 Survey Paper, Special Issue: Intelligent Music Systems and Applications and Regular Papers

2016
Volume 8 Issue 1, October 2016
Volume 7 Issue 4, July 2016 Special Issue on Crowd in Intelligent Systems, Research Note/Short Paper and Regular Papers
Volume 7 Issue 3, April 2016 Regular Papers, Survey Papers and Special Issue on Recommender System Benchmarks
Volume 7 Issue 2, January 2016 Special Issue on Causal Discovery and Inference

2015
Volume 7 Issue 1, October 2015
Volume 6 Issue 4, August 2015 Regular Papers and Special Section on Intelligent Healthcare Informatics
Volume 6 Issue 3, May 2015 Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd Intelligence
Volume 6 Issue 2, May 2015 Special Section on Visual Understanding with RGB-D Sensors
Volume 6 Issue 1, April 2015
Volume 5 Issue 4, January 2015 Special Sections on Diversity and Discovery in Recommender Systems, Online Advertising and Regular Papers

2014
Volume 5 Issue 3, September 2014 Special Section on Urban Computing
Volume 5 Issue 2, April 2014 Special Issue on Linking Social Granularity and Functions

2013
Volume 5 Issue 1, December 2013 Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Volume 4 Issue 4, September 2013 Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Volume 4 Issue 3, June 2013 Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
Volume 4 Issue 2, March 2013 Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
Volume 4 Issue 1, January 2013 Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context

2012
Volume 3 Issue 4, September 2012
Volume 3 Issue 3, May 2012
Volume 3 Issue 2, February 2012

2011
Volume 3 Issue 1, October 2011
Volume 2 Issue 4, July 2011
Volume 2 Issue 3, April 2011
Volume 2 Issue 2, February 2011
Volume 2 Issue 1, January 2011

2010
Volume 1 Issue 2, November 2010
Volume 1 Issue 1, October 2010
 
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