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Mobile Social Multimedia Analytics in the Big Data Era

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)

Learning User Attributes via Mobile Social Multimedia Analytics

Learning user attributes from mobile social media is a fundamental basis for many applications, such as personalized and targeting services. A large... (more)

Large Sparse Cone Non-negative Matrix Factorization for Image Annotation

Image annotation assigns relevant tags to query images based on their semantic contents. Since Non-negative Matrix Factorization (NMF) has the strong... (more)

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

Having benefited from tremendous growth of user-generated content, social annotated tags get higher importance in the organization and retrieval of... (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)

Cost-Optimized Microblog Distribution over Geo-Distributed Data Centers

The unprecedent growth of microblog services poses significant challenges on network traffic and service latency to the underlay infrastructure (i.e.,... (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)

NEWS

Recent TIST News: 

ACM Transactions on Intelligent Systems and Technology (TIST) is ranked No.1 in all ACM journals in terms of citations received per paper. Each paper published at TIST in the time span (from Jan. 2010 to Dec. 2014) has received 18 citations on average in ACM Digital Library in the past fiscal year (from July 1 2015 to June 30 2016).  

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 (2016)

  • - Impact Factor: 2.4
  • - 5-year Impact Factor: 9.15
  • - Avg. Citations in ACM DL: 18 

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

Cyber Security and the Role of Intelligent Systems in Addressing its Challenges

TensorBeat: Tensor Decomposition for Monitoring Multi-Person Breathing Beats with Commodity WiFi

Spotting Trip Purposes from Taxi Trajectories: A General Probabilistic Model

What is the purpose of a trip? What are the unique human mobility patterns and spatial contexts in or near the pickup points and delivery points of trajectories for a specific trip purpose? Many studies have modeled human mobility patterns of urban regions; these analytics mainly focus on interpreting the semantic meanings of geographic topics at an aggregated level. However, at an individual level, given the lack of information about destination activities, it is challenging to convert these studies into effective tools for inferring trip purposes. To address this challenge, in this paper, we study large-scale taxi trajectories of individuals, and we identify some interesting observations. First, the urban forms (i.e., geographic topics and corresponding portfolios) of surrounding areas of pickup points and delivery points closely relate to trip proposes. Second, given a pickup point, spotting trip purposes can be reduced into selecting the best delivery point that should be linked to the pickup point in terms of the matching degree between trip purpose and spatiotemporal contexts. Existing human mobility modeling methods do not exhibit these types of observations, even at an individual level. Along these lines, we develop a general analytic framework for spotting trip purposes from massive taxi GPS trajectories. Specifically, we first augment the origin and destination points of trajectories by attaching POI topics and corresponding portfolios in origin and destination regions. We then propose a generative probabilistic model of taxi trajectories by capturing the intercorrelations among trajectory directions, POI topical portfolios of O-D pairs, and pickup and arrival times. This model consists four analogies: (i) POI as a word; (ii) origin and destination neighborhoods as O-D documents; (iii) trajectory as a directed link between O-D documents; (vi) trip purpose as a link type. In this way, we can group trajectories into trip clusters in terms of trip purposes, and ultimately exploit spectrum analysis to identify the semantic meanings of latent purposes. Finally, we present extensive experiments with the real-world taxi trajectory data of New York City to demonstrate the effectiveness of our proposed method for spotting trip purposes at an individual level.

Scalable Urban Mobile Crowdsourcing: Handling Uncertainty in Worker Movement

In this work, we investigate effective ways of utilizing crowdworkers in providing various urban services. The task recommendation platform that we design can match tasks to crowdworkers based on workers' historical trajectories and time budget limits, thus making recommendations personal and efficient. One major challenging we manage to address is the handling of crowdworker's trajectory uncertainties. In this work, we explicitly allow multiple routine routes to be probabilistically associated with each worker. We formulate this problem as an integer linear program whose goal is to maximize the expected total utility achieved by all workers. We further exploit the separable structures of the formulation and apply the Lagrangian relaxation technique to scale up computation. Numerical experiments have been performed over the instances generated using the realistic public transit dataset in Singapore. The results show that we can find significantly better solutions than the deterministic formulation, and in most cases we can find solutions that are very close to the theoretical performance limit. To demonstrate the practicality of our approach, we deployed our recommendation engine to a campus-scale field trial, and we demonstrate that workers receiving our recommendations incur fewer detours and complete more tasks, and are 20% more efficient against workers relying on their own planning. This is achieved despite having highly uncertain worker trajectories. We also demonstrate how to further improve the robustness of the system by using a simple multi-coverage mechanism.

Risk-Sensitive Stochastic Orienteering Problems for Trip Optimization in Urban Environments

Orienteering Problems (OPs) are used to model many routing and trip planning problems. OPs are a variant of the well-known traveling salesman problem where the goal is to compute the highest reward path that includes a subset of vertices and has an overall travel time less than a specified deadline. However, the applicability of OPs is limited due to the assumption of deterministic and static travel times. To that end, \cite{campbell:11} extended OPs to Stochastic OPs (SOPs) to represent uncertain travel times. In this paper, we make the following key contributions: (1)~we extend SOPs to Dynamic SOPs (DSOPs), which allow for time dependent travel times; (2)~we introduce a new objective criterion for SOPs and DSOPs to represent a percentile measure of risk; (3)~we provide non-linear optimization formulations along with their linear equivalents for solving the risk-sensitive SOPs and DSOPs; (4)~we provide a local search mechanism for solving the risk-sensitive SOPs and DSOPs; and (5)~we provide results on existing benchmark problems and a real-world theme park trip planning problem.

TIST 8:4: Guest Editor Introduction

Using Online Geotagged and Crowdsourced Data to Understand Human Offline Behavior in the City: An Economic Perspective

The pervasiveness of mobile technologies today have facilitated the creation of massive online crowdsourced and geotagged data from individual users at different locations in the city. Such ubiquitous user-generated data allow us to study the social and behavioral trajectories of individuals across both digital and physical environments. This information, combined with traditional economic and behavioral indicators in the city (e.g., store purchases, restaurant visits, parking), can help us better understand human behavior and interactions with cities. In this study, we take an economic perspective and focus on understanding human economic behavior in the city by examining the economic value from crowdsourced and geotaggged data. Specifically, we extract multiple traffic and human mobility features from publicly available data sources using natural language processing and geo-mapping techniques, and examine the effects of both static and dynamic features on economic outcome of local businesses. Our study is instantiated on a unique dataset of restaurant bookings from OpenTable for 3,187 restaurants in New York City from November 2013 to March 2014. Our results suggest that foot traffic can increase local popularity and business performance, while mobility and traffic from automobiles may hurt local businesses, especially the well-established chains and high-end restaurants. We also find that on average one more street closure nearby leads to a 4.7% decrease in the probability of a restaurant being fully booked during the dinner peak. Our study demonstrates the potential of how to best make use of the large volumes and diverse sources of crowdsourced and geotagged user-generated data to create matrices to predict local economic demand in a manner that is fast, cheap, accurate, and meaningful.

A Multiagent-based Approach for Vehicle Routing by Considering both Arriving on Time and Total Travel Time

Arriving on time and total travel time are two important properties for vehicle routing. Existing route guidance approaches always consider them independently because they may conflict with each other. In this paper, we develop a decentralized multiagent-based vehicle routing approach where vehicle agents follow the local route guidance by infrastructure agents at each intersection, and infrastructure agents perform the route guidance by solving a route assignment problem. It integrates the two properties by expressing them as two objective terms of the route assignment problem. Regarding arriving on time, it is formulated based on the probability tail model, which aims to maximize the probability of reaching destination before deadline. Regarding total travel time, it is formulated as a weighted quadratic term, which aims to minimize the expected travel time from the current location to the destination based on the potential route assignment. The weight for total travel time is designed to be comparatively large if the deadline is loose. Additionally, we improve the proposed approach in several aspects, including travel time prediction, computational efficiency and infrastructure agent communication. Experimental results on real road networks justify its ability to increase the average probability of arriving on time, reduce total travel time and enhance the overall routing performance.

Exploring Indoor White Spaces in Metropolises

It is a promising vision to utilize white spaces, i.e., vacant VHF and UHF TV channels, to satisfy skyrocketing wireless data demand in both outdoor and indoor scenarios. While most prior works have focused on exploring outdoor white spaces, the indoor story is largely open for investigation. Motivated by this observation and that 70% of the spectrum demand comes from indoor environments, we carry out a comprehensive study of exploring indoor white spaces. We first present a large-scale measurement of outdoor and indoor TV spectrum occupancy in 30+ diverse locations in a typical metropolis Hong Kong. Our measurement results confirm abundant white spaces available for exploration in a wide range of areas in metropolises. In particular, more than 50% and 70% of the TV spectrum are white spaces in outdoor and indoor scenarios, respectively. While there are substantially more white spaces in indoor scenarios than in outdoor scenarios, there is no effective solution for identifying indoor white spaces. To fill in this gap, we propose the first system WISER (for White-space Indoor Spectrum EnhanceR), to identify and track indoor white spaces in a building, without requiring user devices to sense the spectrum. We discuss the design space of such system and justify our design choices using intensive real-world measurements. We design the architecture and algorithms to address the inherent challenges. We build a WISER prototype and carry out real-world experiments to evaluate its performance. Our results show that WISER can identify 30%-40% more indoor white spaces with negligible false alarms, as compared to alternative baseline approaches.

From Electromyogram to Password: Exploring the Privacy Impact of Wearables in Augmented Reality

With the increasing popularity of augmented reality (AR) services, providing seamless human-computer interactions in the AR setting has received notable attentions in the industry. Gesture control devices have recently emerged to be the next great gadgets for AR due to its unique ability to enable computer interaction with day-to-day gestures. While these AR devices are bringing revolutions to our interaction with the cyber world, it is also important to consider potential privacy leakages from these always-on wearable sensors. Although the always-on gesture sensors are frequently quoted as a privacy concern, there hasn't been any study on information leakage of these devices. In this paper, we present our study on side channel information leakage of the most popular gesture control device, Myo. Using signals recorded from the electromyography (EMG) sensor and accelerometers on Myo, we can recover sensitive information such as passwords typed on a keyboard and PIN sequence entered through a touchscreen. EMG signal records subtle electric current of muscle contractions. On Myo, these coarse-grained signals are used for recognizing few number of gestures. We design novel algorithms based on dynamic cumulative sum and wavelet transform to extract exact timestamps of finger movements. Furthermore, Hudgins feature set is used with support vector machine to classify recorded signals segments into individual fingers or numbers. We also apply coordinate transformation techniques to recover fine-grained spatial information with low-fidelity outputs from the sensor. We demonstrate the information leakage using data collected from a group of volunteers. Our results show that there is severe privacy leakage from these commodity wearable sensors. Our system recovers complex passwords constructed with lower case letters, upper case letters, numbers and symbols with a mean success rate of 91%.

illiad: InteLLigent Invariant and Anomaly Detection in Cyber Physical Systems

Cyber physical systems (CPSs) are today ubiquitous in urban environments. Such systems now serve as thebackbone to numerous critical infrastructure applications, from smart grids to IoT installations. Scalableand seamless operation of such CPSs requires sophisticated tools for monitoring the time series progres-sion of the system, dynamically tracking relationships, and issuing alerts about anomalies to operators. Wepresent an online monitoring system (illiad) that models the state of the CPS as a function of its relation-ships between constituent components, using a combination of model-based and data-driven strategies. Inaddition to accurate inference for state estimation and anomaly tracking,illiadis able to exploit the un-derlying network structure of the CPS (wired or wireless) for state estimation purposes. We demonstratethe application ofilliadto two diverse settings: a wireless sensor motes application and an IEEE 33-busmicrogrid

DMAD: Data-Driven Measuring of Wi-Fi Access Point Deployment in Urban Spaces

Wireless networks offer many advantages over wired local area networks such as scalability and mobility. Strategically deployed wireless networks can achieve multiple objectives like traffic offloading, network coverage and indoor localization. To this end, various mathematical models and optimization algorithms have been proposed to find optimal deployments of access points (APs) for different objectives, like coverage ratio. However, wireless signals can be blocked by the human body, especially in crowded urban spaces. The impact of human beings on wireless coverage cannot be easily analyzed by existing methods. Site surveys are too time-consuming and labor-intensive to conduct. It is infeasible for simulation methods to predict the number of people. As a result, the real coverage of an on-site AP deployment may shrink to some degree and lead to unexpected dead spots (areas without wireless coverage). These dead spots are undesirable, since they degrade the user experience in network service continuity on one hand, and on the other hand paralyze some applications and services like tracking and monitoring when users are in these areas. In this paper, we propose DMAD, a Data-driven Measuring of Access point Deployment, which can not only find potential dead spots of an on-site AP deployment but also quantify their severity. DMAD utilizes simple Wi-Fi data collected from the on-site AP deployment and shop data from the Internet. We firstly classify static devices and mobile devices using a decision-tree classifier. Then locate these devices to shop-level locations based on shop popularities, wireless signals, and visit duration. Lastly, for each location, we estimate the probability of dead spots in different time slots and derive their severity combining the probability and human density. The analysis of Wi-Fi data from static devices indicates that the Pearson Correlation Coefficient of wire- less coverage status and the number of on-site people is over 0.7, which confirms that human beings may have a significant impact on wireless coverage. We also conduct extensive experiments in a large shopping mall in Shenzhen. The evaluation results demonstrate that DMAD can find around 70% of dead spots with a precision of over 70%.

A Comfort-Based Approach to Smart Heating and Air Conditioning

In this paper, we address the interrelated challenges of predicting user comfort and using this to reduce energy consumption in smart heating, ventilation and air conditioning (HVAC) systems. At present, such systems use simple models of user comfort when deciding on a set point temperature. Being built using broad population statistics, these models generally fail represent individual users preferences, resulting in poor estimates of the users preferred temperatures. To address this issue, we propose the Bayesian Comfort Model (BCM). This personalised thermal comfort model using a Bayesian network learns from a users feedback, allowing it to adapt to the users individual preferences over time. We further propose an alternative to the ASHRAE 7-point scale used to assess user comfort. Using this model, we create an optimal HVAC control algorithm that minimizes energy consumption while preserving user comfort. Through an empirical evaluation based on the ASHRAE RP-884 data set and data collected in a separate deployment by us, we show that our model is consistently 13.2 to 25.8% more accurate than current models and how using our alternative comfort scale can increase our models accuracy. Through simulations we show that using this model, our HVAC control algorithm can reduce energy consumption by 7.3% to 13.5% while decreasing user discomfort by 24.8% simultaneously.

GeoBurst+: Effective and Real-Time Local Event Detection in Geo-Tagged Tweet Streams

The real-time discovery of local events (e.g., protests, disasters) has been widely recognized as a fundamental socioeconomic task. Recent studies have demonstrated that the geo-tagged tweet stream serves as an unprecedentedly valuable source for local event detection. Nevertheless, how to effectively extract local events from massive geo-tagged tweet streams in real time remains challenging. To bridge the gap, we propose a method for effective and real-time local event detection from geo-tagged tweet streams. Our method, named GeoBurst+, first leverages a novel cross-modal authority measure to identify several pivots in the query window. Such pivots reveal different geo-topical activities and naturally attract similar tweets to form candidate events. GeoBurst+ further summarizes the continuous stream and compares the candidates against the historical summaries to pinpoint truly interesting local events. Better still, as the query window shifts, GeoBurst+ is capable of updating the event list with little time cost, thus achieving continuous monitoring of the stream. We used crowdsourcing to evaluate GeoBurst+ on two million-scale data sets, and found it significantly more effective than existing methods while being orders of magnitude faster.

A Traffic Flow Approach to Early Detection of Gathering Events: Comprehensive Results

Given a spatial field and the traffic flow between neighboring locations, the early detection of gathering events (EDGE) problem aims to discover and localize a set of most likely gathering events. It is important for city planners to identify emerging gathering events which might cause public safety or sustainability concerns. However, it is challenging to solve the EDGE problem due to numerous candidate gathering footprints in a spatial field and the non-trivial task to balance pattern quality and computational efficiency. Prior solutions to model the EDGE problem lack the ability to describe the dynamic flow of traffic and the potential gathering destinations because they rely on static or undirected footprints. In our recent work, we modeled the footprint of a gathering event as a Gathering Graph (G-Graph), where the root of the directed acyclic G-Graph is the potential destination and the directed edges represent the most likely paths traffic takes to move towards the destination. We also proposed an efficient algorithm called {SmartEdge} to discover the most likely non-overlapping G-Graphs in the given spatial field.However, it is challenging to perform a systematic performance study of the proposed algorithm, due to unavailability of the ground truth of gathering events. In this paper, we introduce an event simulation mechanism, which makes it possible to conduct a comprehensive performance study of the SmartEdge algorithm. We measure the quality of the detected patterns, in a systematic way, in terms of timeliness and location accuracy. The results show that, on average, the SmartEdge algorithm is able to detect patterns within a grid cell away (less than 500 meters) of the simulated events and detect patterns of the simulated events as early as 10 minutes prior to the first arrival to the gathering event.

Multi-Hypergraph Consistent Sparse Coding

Sparse representation has been a powerful technique for modeling high-dimensional data. As an unsupervised technique to extract sparse representations, sparse coding encodes the original data into a new sparse code space, and simultaneously learns a dictionary representing high-level semantics. Existing methods have considered local manifold within high-dimensional data using graph/hypergraph Laplacian regularization, and more from the manifold could be utilized to improve the performance. In this paper, we propose to further regulate the sparse coding that learned sparse codes can well reconstruct the hypergraph structure. As a result, we add a novel hypergraph consistency regularization term (HC) by minimizing the reconstruction error of the hypergraph incidence or weight matrix. Moreover, we extend the proposed regularization term HC to multi-hypergraph consistent sparse coding (MultiCSC) to automatically select the optimal manifold structure under the multi-hypergraph learning framework. We show that the optimization of MultiCSC can be solved efficiently, and those several existing sparse coding methods can fit into the general framework of MultiCSC as special cases. As a case study, hypergraph incidence consistent sparse coding is applied to perform semi-auto image tagging, demonstrating the effectiveness of hypergraph consistency regulation. We perform further experiments using MultiCSC for image clustering, which outperforms a number of baselines.

Transfer Learning for Behavior Ranking

Intelligent recommendation has been well recognized as one of the major approaches to address the information overload problem in the big data era. A typical intelligent recommendation engine usually consists of three major components, i.e., data as the main input, algorithms for preference learning, and system for user interaction and high-performance computation. We observe that the data (e.g., users' behavior) are usually in different forms such as examinations (e.g., browse and collection) and ratings, where the former are often much more abundant than the latter. Although the data are in different representations, they are both related to users' true preferences and are also deemed complementary to each other for preference learning. However, very few ranking or recommendation algorithms have been developed to exploit such two types of user behavior. In this paper, we focus on jointly modeling the examination behavior and rating behavior and develop a novel and efficient ranking-oriented recommendation algorithm accordingly. Firstly, we formally define a new recommendation problem termed {\em behavior ranking} (BR), which aims to build a ranking-oriented model by exploiting both the examination behavior and rating behavior. Secondly, we develop a simple and generic {\em transfer to rank} (ToR) algorithm for behavior ranking, which transfers knowledge of candidate items from a global preference learning task to a local preference learning task. Compared with the previous work on integrating heterogeneous user behavior, our ToR algorithm is the first ranking-oriented solution, which can effectively generate recommendations in a more direct manner than those regression-oriented methods. Extensive empirical studies show that our ToR algorithm performs significantly more accurate than the state-of-the-art methods in most cases. Furthermore, our ToR algorithm is very efficient in terms of the time complexity, which is similar to those for homogeneous user behavior alone.

A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing

Spatial Crowdsourcing (SC) is a novel platform that engages individuals in the act of collecting various types of spatial data. This method of data collection can significantly reduce cost and turnover time, and is particularly useful in environmental sensing, where traditional means fail to provide fine-grained field data. In this study, we introduce hyperlocal spatial crowdsourcing, where all workers who are located within the spatiotemporal vicinity of a task are eligible to perform the task, e.g., reporting the precipitation level at their area and time. In this setting, there is often a $budget$ constraint, either for every time period or for the entire campaign, on the number of workers to activate to perform tasks. The challenge is thus to maximize the number of assigned tasks under the budget constraint, despite the dynamic arrivals of workers and tasks. We introduce a taxonomy of several problem variants, such as budget-per-time-period vs. budget-per-campaign and binary-utility vs. distance-based-utility. We study the hardness of the task assignment problem in the offline setting and propose online heuristics which exploits the spatial and temporal knowledge acquired over time. Our experiments are conducted systematically with datasets generated by spatial crowdsourcing workbench and extensive results show the effectiveness and efficiency of our proposed solutions.

Taking the Pulse of US College Campuses with Location-Based Anonymous Mobile Apps

We deploy GPS hacking in conjunction with location-based mobile apps to passively survey users in targeted geographical regions. Speci cally, we investigate surveying students with Yik Yak, an anonymous mobile app that is popular on US college campuses. In addition to being campus-centric, Yik Yak's anonymity allows students to express themselves candidly without self-censorship. We collect over 1.6 million Yik Yak messages ("yaks") from a diverse set of 50 college campuses in the United States. We use natural language processing to determine the sentiment (positive, negative, or neutral) of all of the yaks. We employ supervised machine learning to predict the gender of the authors of the yaks, and then analyze how sentiment di ers among the two genders on college campuses. We also use supervised machine learning to classify all the yaks into nine topics, and then investigate which topics are most popular throughout the US, and how topic popu larity varies on the di erent campuses.

Personalized Air Travel Prediction: A Multi-factor Perspective

Human mobility analysis is one of the most important research problems in the field of urban computing. Existing research mainly focuses on the intra-city ground travel behavior modeling, while the inter-city air travel behavior modeling has been largely ignored. Actually, the inter-city travel analysis can be of equivalent importance and complementary to the intra-city travel analysis. Understanding the massive passengers' air travel behaviors delivers intelligence for airlines' precision marketing and related socioeconomic activities, such as airport planning, emergency management, local transportation planning, and tourism-related businesses. Moreover, it provides opportunities to study the characteristics of cities and the mutual relationships between cities. However, modeling and predicting air travelers' behavior is challenging due to the complex factors of the market situation and individual characteristics of customers (e.g. airlines' market share, customer membership, and travelers' intrinsic interests on destinations). To this end, in this paper, we present a systematic study on the personalized air travel prediction problem, namely where a customer will fly to and which airline carrier to fly with, by leveraging real-world anonymized Passenger Name Record (PNR) data. Specifically, we first propose a Relational Travel Topic Model (RTTM), which combines the merits of latent factor model and neighborhood-based method, to uncover the personal travel preferences of aviation customers and the latent travel topics of air routes simultaneously. Then we present a Multi-Factor Travel Prediction (MFTP) framework, which fuses complex factors of the market situation and individual characteristics of customers, to predict airline customers' personalized travel demands. Experimental results on two real-world PNR datasets demonstrate the effectiveness of our approach on both latent travel topics discovery and customer travel prediction.

DUCT: An Upper Confidence Bound Approach to Distributed Constraint Optimization Problems

We propose a distributed upper confidence bound approach, DUCT, for solving distributed constraint optimization problems. We compare four variants of this approach with a baseline random sampling algorithm, as well as other complete and incomplete algorithms for DCOPs. Under general assumptions, we theoretically show that the solution found by DUCT after T steps is approximately T1-close to the optimal. Experimentally, we show that DUCT matches the optimal solution found by the well- known DPOP and O-DPOP algorithms on moderate-size problems, while always requiring less agent communication. For larger problems, where DPOP fails, we show that DUCT produces significantly better solutions than local, incomplete algorithms. Overall we believe that DUCT is a practical, scalable algorithm for complex DCOPs.

Bibliometrics

Publication Years 2010-2017
Publication Count 454
Citation Count 5327
Available for Download 454
Downloads (6 weeks) 7203
Downloads (12 Months) 47791
Downloads (cumulative) 207569
Average downloads per article 457
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
Xing Xie 7
Dacheng Tao 7
Enhong Chen 6
Nicholasjing Yuan 5
Tatseng Chua 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
VS Subrahmanian 3
Qi Tian 3
Wenchih Peng 3
Tao Li 3
Pablo Castells 2
Meir Kalech 2
Daxin Jiang 2
Subbarao Kambhampati 2
Jamal Bentahar 2
Kyumin Lee 2
Hasan Cam 2
Robin Cohen 2
Ya'akov Gal 2
Shuaiqiang Wang 2
Chong Peng 2
Qingzhong Liu 2
Jiawei Han 2
Luan Tang 2
JiLei Tian 2
Claudio Biancalana 2
Giuseppe Sansonetti 2
Mahdi Jalili 2
Anlei Dong 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
Chihjen Lin 2
Diane Cook 2
Defu Lian 2
Jie Cheng 2
Yoshinobu Kawahara 2
Quan Fang 2
Elena Baralis 2
Tania Cerquitelli 2
Robin Cohen 2
Mahmud Hossain 2
SungWook Yoon 2
Vincent Tseng 2
Hongxun Yao 2
Zhiwen Yu 2
Paulo Shakarian 2
Hongyuan Zha 2
Sihong Xie 2
Haggai Roitman 2
Liyan Zhang 2
Alex Rogers 2
Xueqi Cheng 2
Alberto Del Bimbo 2
Yongdong Zhang 2
Jian Pei 2
Amin Javari 2
Amit Chopra 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
Manish Marwah 2
Hanqing Lu 2
Tao Mei 2
James Caverlee 2
Thomas Dietterich 2
Jalal Mahmud 2
Nathan Eagle 2
Xuning Tang 2
Rino Falcone 2
Jinshi Cui 2
Jia Zeng 2
Dana Nau 2
Shoude Lin 2
Sushil Jajodia 2
Ling Guan 2
Michael Fire 2
Neil Yorke-Smith 2
Laiwan Chan 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
Rajesh Ganesan 2
Vito Ostuni 2
Jun Ma 2
Jiuyong Li 2
Sarvapali Ramchurn 2
Yuichi Motai 2
Masaki Aono 2
Yihsuan Yang 2
David Thompson 2
Benno Stein 2
Alejandro Bellogín 2
Bingbing Ni 2
Jeffrey Nichols 2
Daqing Zhang 2
Tommaso Noia 2
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Weiwei Cui 1
Pierre Rouille 1
Geoffrey Holmes 1
Yuhang Zhao 1
Bingqing Qu 1
Gerd Stumme 1
David Glass 1
Toon De Pessemier 1
Michelle Zhou 1
Liangliang Cao 1
José Cortizo 1
Janardhan Doppa 1
Bhavesh Shrestha 1
Victor Lesser 1
Daniel McFarlane 1
Yong Yu 1
Yosi Mass 1
Hal Daumé 1
Richong Zhang 1
Wenjun Zhou 1
Chihchung Chang 1
Bernardo Huberman 1
Dana Nau 1
Kyumin Lee 1
Hongtai Li 1
Oded Maimon 1
Wangsheng Zhang 1
Brent Longstaff 1
Joshua Selsky 1
Atesmachew Hailegiorgis 1
Aris Anagnostopoulos 1
Guillermo Jiménez-Díaz 1
Fatih Gedikli 1
Hongbin Zha 1
Yuchun Shen 1
Furu Wei 1
Ya Zhang 1
Marjan Momtazpour 1
Jason Hong 1
Licia Capra 1
Ouri Wolfson 1
Eoghan Furey 1
Aonghus Lawlor 1
Dan Lin 1
Juan Cao 1
Byron Gao 1
Theodoros Semertzidis 1
Martin Bockle 1
Waitat Fu 1
Yubin Kim 1
Jaime Teevan 1
Patrick De Boer 1
Boi Faltings 1
Alina Huldtgren 1
Ingrid Heynderickx 1
Paweł Woźniak 1
Mohammad Obaid 1
Jiashi Feng 1
Teng Li 1
Tao Li 1
Ankit Shah 1
Tao Gu 1
Jiangbo Jia 1
Xingshe Zhou 1
Shazia Sadiq 1
Anna Monreale 1
Zhenmin Tang 1
Franco Nardini 1
Mingli Song 1
Jiajun Bu 1
Ah Tsoi 1
Stevende Jong 1
Yuesong Wang 1
Matthew Kyan 1
Guoyu Sun 1
Paisarn Muneesawang 1
Yufei Wang 1
Tianzhu Zhang 1
Nadia Figueroa 1
Kuiyu Chang 1
Chao Xu 1
Paul Schermerhorn 1
Matthias Scheutz 1
Abder Benaskeur 1
Alex Smola 1
Saisai Ma 1
Siddhartha Ghosh 1
Yuchin Juan 1
Dawei Song 1
Daniel Bryce 1
Michael Verdicchio 1
Na Shan 1
Hadrien Hours 1
Ernst Biersack 1
Patrick Loiseau 1
Marina Demeshko 1
Carla Gomes 1
Michela Milano 1
Ming Ji 1
Yintao Yu 1
Matthew Boyce 1
Michael Steinbach 1
Yang Mu 1
Hengshu Zhu 1
Tieyan Liu 1
Marco Ribeiro 1
Anísio Lacerda 1
Adriano Veloso 1
Ümit Çatalyürek 1
Weiming Hu 1
Bin Chen 1
Jinbo Bi 1
Yu Wu 1
Stephen Armeli 1
Thomas Hoens 1
Chandan Reddy 1
Amos Azaria 1
Bin Wu 1
Wenyuan Zhu 1
Bo Long 1
Lihong Li 1
Filippo Bistaffa 1
Alessandro Farinelli 1
Jesús Cerquides 1
Juan Rodríguez-Aguilar 1
Tudor Dumitraş 1
Avishai Wool 1
Xitong Yang 1
Timothy Norman 1
Olivier Colot 1
Elisa Marengo 1
Waynexin Zhao 1
Qun Jin 1
Huijing Zhao 1
Xiangfeng Luo 1
Wangchien Lee 1
Alice Leung 1
Chenghua Lin 1
Paola Mello 1
Enrico Pontelli 1
Marta Arias 1
Ramon Xuriguera 1
Janyl Jumadinova 1
Xing Xie 1
Ching Law 1
José García-Macías 1
Paolo Garza 1
Sergio Oramas 1
Massimo Mecella 1
Feng Wu 1
Payam Barnaghi 1
Amit Sheth 1
Miyoung Kim 1
Liping Xie 1
David Hayden 1
Markus Mühling 1
Yujin Zhang 1
Xianming Liu 1
Shiguang Shan 1
Myunghoon Suk 1
Shaohui Liu 1
Mary Pendleton Hoffer 1
Daniel Schuster 1
Benjamin Hung 1
Stephan Kolitz 1
Yakov Kronrod 1
Aurélien Max 1
Anne Vilnat 1
Tobias Höllerer 1
Yi Zhang 1
Hossein Hajimirsadeghi 1
Hadi Moradi 1
Siegfried Handschuh 1
Jing Bai 1
Carolina Batista 1
Jiankang Deng 1
Dingqi Yang 1
Yuanzhuo Wang 1
Tie Luo 1
Guangming Guo 1
Luc Martens 1
Paolo Cremonesi 1
Yue Zhou 1
Tanzeem Choudhury 1
Guan Wang 1
Jiawei Han 1
Francisco Carrero 1
Wengkeen Wong 1
Huzaifa Zafar 1
Kenneth Whitebread 1
Zhenxing Wang 1
Linyun Fu 1
Scott DuVall 1
Aristidis Pappaioannou 1
Michal Feldman 1
Wangchien Lee 1
Zheng Song 1
Jian Ma 1
Zhaohui Wu 1
Leye Wang 1
J Gibson 1
Chengkang Hsieh 1
John Jenkins 1
Zhengdong Lu 1
Michael O’Mahony 1
Claudio Cioffi-Revilla 1
Zhen Liao 1
Hongan Wang 1
Peter Prettenhofer 1
Hilal Khashan 1
Shiwan Zhao 1
Fernando Díez 1
Yoshiyuki Inagaki 1
Alena Neviarouskaya 1
Wenning Kuo 1
Alexei Pozdnoukhov 1
Jintao Li 1
Jiankai Sun 1

Affiliation Paper Counts
Ryukoku University 1
University of Connecticut Health Center 1
University of Lausanne 1
Federal University of Amazonas 1
University of Macedonia 1
Demokritos National Centre for Scientific Research 1
University of Michigan 1
Anhui University 1
National Taitung University Taiwan 1
University of Sheffield 1
Ehime University 1
University of Haifa 1
University of Perugia 1
Joint Institute for Nuclear Research, Dubna 1
Instituto Superior Tecnico 1
Southeast University China, Nanjing 1
University of Auckland 1
Bogazici University 1
University of Houston 1
University of Pennsylvania 1
University of Koblenz-Landau 1
Guangdong University of Technology 1
Northwestern University 1
Smithsonian National Museum of Natural History 1
University of Texas at El Paso 1
Hebrew University of Jerusalem 1
Osaka Prefecture University 1
Duke University 1
Vrije Universiteit Amsterdam 1
Birkbeck University of London 1
Educational Testing Service 1
IBM Almaden Research Center 1
Wayne State University 1
Northeast Normal University China 1
Central European University 1
Harvard University 1
University of Arizona 1
Rissho University 1
Istituto Di Calcolo E Reti Ad Alte Prestazioni, Rende 1
The University of British Columbia 1
Dartmouth College 1
Hohai University 1
The University of Western Ontario 1
Citigroup 1
Lingnan University 1
King's College London 1
Center for Mathematics and Computer Science - Amsterdam 1
University of Messina 1
University of Shizuoka 1
Aoyama Gakuin University 1
United States National Science Foundation 1
Ionian University 1
University of Passau 1
Eastman Kodak Company 1
University of Saskatchewan 1
General Electric Company 1
New York State Museum 1
Beijing Institute of Technology 1
Charles Stark Draper Lab Inc 1
Defence Research and Development Canada 1
Nankai University 1
Vienna University of Technology 1
Washington State University Pullman 1
Office of Naval Research 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
Nanjing University of Aeronautics and Astronautics 1
Aalborg University 1
Naresuan University 1
Politecnico di Milano 1
Shanghai University 1
Soka University 1
Ecole Centrale Paris 1
National University of Defense Technology China 1
University of Fribourg 1
National Central University Taiwan 1
Dublin City University 1
Catholic University of Leuven, Leuven 1
The University of North Carolina at Chapel Hill 1
University of Cincinnati 1
University of Udine 1
Institute of Intelligent Machines Chinese Academy of Sciences 1
Capital Medical University China 1
United States Military Academy 1
University of Quebec in Montreal 1
Lanzhou University 1
Berlin University of Applied Sciences 1
Fairleigh Dickinson University 1
Research Organization of Information and Systems National Institute of Informatics 1
University of Hawaii System 1
University of Chicago 1
University of Southern California 1
European Space Agency - ESA 1
NICTA 1
University of California, Riverside 1
Institute of Applied Physics and Computational Mathematics 1
Columbia University 1
Texas State University-San Marcos 1
Ecole des Mines de Paris 1
Hosei University 1
Rutgers, The State University of New Jersey 1
Boeing Corporation 1
Santa Fe Institute 1
Michigan State University 1
University of Western Australia 1
Indian Institute of Technology Roorkee 1
North Dakota State University 1
University of Electro-Communications 1
Pontifical Catholic University of Rio de Janeiro 1
University of Jyvaskyla 1
University of Chittagong 1
University of Seville 1
Mehran University of Engineering & Technology 1
University of Sousse 1
Nanjing University of Information Science and Technology 1
Know-Center, Graz 1
Institute for Cancer Research and Treatment, Candiolo 1
Reykjavik University 1
Macau University of Science and Technology 1
SONY Computer Science Laboratory, Paris 1
Google Switzerland GmbH 1
Intel Research Laboratories 1
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
Shenzhen University 2
University of Texas at San Antonio 2
University of Manchester 2
King Abdulaziz University 2
University of Lugano 2
University of Missouri-Kansas City 2
University of Wolverhampton 2
University of Brighton 2
Utrecht University 2
National University of Ireland, Galway 2
University of Massachusetts Dartmouth 2
Universite des Sciences et Technologies de Lille 2
University of Kent 2
RMIT University 2
Technical University of Berlin 2
Open University 2
University of Zurich 2
University of Antwerp 2
King Saud University 2
Telecom Bretagne 2
Beihang University 2
Aston University 2
University of Hawaii at Hilo 2
Aristotle University of Thessaloniki 2
University of Southern California, Information Sciences Institute 2
Dalhousie University 2
Academia Sinica Taiwan 2
National Tsing Hua University 2
Xiamen University 2
Technical University of Dresden 2
Tamkang University 2
Singapore Management University 2
University of Nebraska at Omaha 2
University of Bristol 2
East China Normal University 2
Communication University of China 2
Ecole d' Ingenieurs Telecom Lille 1 2
Johannes Kepler University Linz 2
Queen Mary, University of London 2
University of Central Florida 2
Waseda University 2
Lancaster University 2
University of Massachusetts Boston 2
University of Ferrara 2
Sam Houston State University 2
Xidian University 2
University of Oxford 2
University of Rochester 2
University of Edinburgh 2
Jerusalem College of Technology 2
Hungarian Academy of Sciences 2
New Mexico Institute of Mining and Technology 2
University of Athens 2
Universite de Rennes 1 2
University of Queensland 2
University of California System 2
NEC Corporation 2
University of Verona 2
University of Dortmund 2
Intel Corporation 2
U.S. Army Research Laboratory 2
American University of Beirut 2
Universite Paris-Est 2
Telecom & Management SudParis 2
SRI International 3
David R. Cheriton School of Computer Science 3
Universiti Sains Malaysia 3
Chalmers University of Technology 3
Universite Pierre et Marie Curie 3
University of Glasgow 3
University College Dublin 3
Brigham Young University 3
Jiangnan University 3
Universitat Politecnica de Catalunya 3
Orebro University 3
University of Wyoming 3
Washington State University 3
Free University of Bozen-Bolzano 3
Universidad Politecnica de Valencia 3
Changchun University of Technology 3
BBN Technologies 3
University of Oregon 3
Philipps-Universitat Marburg 3
Universidad de Jaen 3
Georgia Tech Research Institute 3
University of Stuttgart 3
Kassel University 3
Wright State University 3
Simon Fraser University 3
Xerox Corporation 3
Graz University of Technology 3
New York University 3
University of Macau 3
University of Connecticut 3
University of North Texas 3
Universite Paris-Sud XI 3
University of Utah 3
University of Konstanz 3
University of Teesside 3
Huazhong University of Science and Technology 3
Center For Research And Technology - Hellas 3
Stony Brook University 3
University of Szeged 3
University of Bologna 3
University of Pisa 3
EURECOM Ecole d'Ingenieurs & Centre de Recherche en Systemes de Communication 3
University of California, San Diego 3
Utah State University 3
University of Wisconsin Madison 3
University of Roma La Sapienza 3
Intel Corporation, China 3
Rutgers University-Newark Campus 4
New Mexico State University Las Cruces 4
Bar-Ilan University 4
Hefei University of Technology 4
Toyohashi University of Technology 4
Ohio State University 4
University of Waikato 4
The University of North Carolina at Charlotte 4
University of Vermont 4
University of Adelaide 4
University of Florida 4
University of Pavia 4
University of Washington, Seattle 4
University of Trento 4
Technical University of Lodz 4
Tianjin University 4
University of Calabria 4
Guangxi Normal University 4
University of Liverpool 4
Indiana University 4
West Virginia University 4
University of Notre Dame 4
University of Florence 4
University of Virginia 4
Massachusetts Institute of Technology 4
University of Tehran 4
Swiss Federal Institute of Technology, Zurich 4
National University of Ireland, Maynooth 4
Microsoft Corporation 4
Complutense University of Madrid 4
Sharif University of Technology 4
University of Ottawa, Canada 4
University of California, Santa Barbara 4
University of Miami 4
Korea Advanced Institute of Science & Technology 4
Eindhoven University of Technology 4
Nanjing University 4
Cornell Tech 4
Institute for Infocomm Research, A-Star, Singapore 5
North Carolina State University 5
Tel Aviv University 5
Google Inc. 5
University of California, Irvine 5
Istituto Di Scienze E Tecnologie Della Cognizione, Rome 5
City University of Hong Kong 5
University of Massachusetts Amherst 5
National Cheng Kung University 5
University of Pittsburgh 5
CSIC - Instituto de Investigacion en Inteligencia Artificial 5
University College London 5
Beijing Jiaotong University 5
Osaka University 5
Rensselaer Polytechnic Institute 5
Southern Illinois University at Carbondale 5
Max Planck Institute for Intelligent Systems 5
TECH Lab 5
Shandong University 5
Yahoo Research Barcelona 5
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 5
Pennsylvania State University 6
Virginia Commonwealth University 6
Hong Kong Polytechnic University 6
Washington University in St. Louis 6
Soochow University 6
National Taipei University of Technology 6
Stanford University 6
Hong Kong Baptist University 6
IBM Thomas J. Watson Research Center 6
NEC Laboratories America, Inc. 6
Texas A and M University 6
Missouri University of Science and Technology 6
Biblioteca CICESE 6
TELECOM ParisTech 6
University of Alberta 6
Ryerson University 6
University of South Australia 6
Universitat Pompeu Fabra 6
Oregon State University 7
George Mason University 7
Institute of Automation Chinese Academy of Sciences 7
University of Ulster 7
University of Bari 7
University of Southampton 7
Universidad Autonoma de Madrid 7
University of Texas System 7
HP Labs 8
National Chiao Tung University Taiwan 8
Roma Tre University 8
Drexel University 8
University of Aberdeen 8
University of California, Berkeley 8
University of Illinois at Chicago 8
Ghent University 8
Northwestern Polytechnical University China 9
NASA Ames Research Center 9
Hong Kong University of Science and Technology 9
University of Texas at Austin 9
University of Waterloo 9
Virginia Tech 9
Bauhaus University Weimar 9
Nanjing University of Science and Technology 10
University of Turin 10
Beijing University of Posts and Telecommunications 10
Swiss Federal Institute of Technology, Lausanne 10
University of Minnesota Twin Cities 10
Federal University of Minas Gerais 10
Georgia Institute of Technology 11
University of Melbourne 11
Nokia Corporation 11
Zhejiang University 12
Shanghai Jiaotong University 12
Delft University of Technology 13
Polytechnic Institute of Turin 13
University of Technology Sydney 13
Chinese University of Hong Kong 14
Florida International University 15
Ben-Gurion University of the Negev 15
University of California, Los Angeles 15
Yahoo Research Labs 15
Arizona State University 16
Carnegie Mellon University 16
Nanyang Technological University 17
Harbin Institute of Technology 17
University of Tokyo 17
Tsinghua University 17
Microsoft Research 20
Institute of Computing Technology Chinese Academy of Sciences 20
University of Illinois at Urbana-Champaign 21
National Taiwan University 21
Microsoft Research Asia 21
Peking University 22
Jet Propulsion Laboratory 24
IBM Research 24
University of Maryland 29
National University of Singapore 31
Chinese Academy of Sciences 31
University of Science and Technology of China 32

ACM Transactions on Intelligent Systems and Technology (TIST) - Special Issue: Mobile Social Multimedia Analytics in the Big Data Era and Regular Papers
Archive


2017
Volume 8 Issue 3, April 2017 Special Issue: Mobile Social Multimedia Analytics in the Big Data Era and Regular Papers
Volume 8 Issue 4, March 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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