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Singapore Management University

2019

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Articles 391 - 420 of 425

Full-Text Articles in Physical Sciences and Mathematics

Cryptocurrency Mining On Mobile As An Alternative Monetization Approach, Nguyen Phan Sinh Huynh, Kenny Choo, Rajesh Krishna Balan, Youngki Lee Feb 2019

Cryptocurrency Mining On Mobile As An Alternative Monetization Approach, Nguyen Phan Sinh Huynh, Kenny Choo, Rajesh Krishna Balan, Youngki Lee

Research Collection School Of Computing and Information Systems

Can cryptocurrency mining (crypto-mining) be a practical ad-free monetization approach for mobile app developers? We conducted a lab experiment and a user study with 228 real Android users to investigate different aspects of mobile crypto-mining. In particular, we show that mobile devices have computational resources to spare and that these can be utilized for crypto-mining with minimal impact on the mobile user experience. We also examined the profitability of mobile crypto-mining and its stability as compared to mobile advertising. In many cases, the profit of mining can exceed mobile advertising's. Most importantly, our study shows that the majority (72%) of …

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Verifiable Computation Using Re-Randomizable Garbled Circuits, Qingsong Zhao, Qingkai Zeng, Ximeng Liu, Huanliang Xu Feb 2019

Verifiable Computation Using Re-Randomizable Garbled Circuits, Qingsong Zhao, Qingkai Zeng, Ximeng Liu, Huanliang Xu

Research Collection School Of Computing and Information Systems

Yao's garbled circuit allows a client to outsource a function computation to a server with verifiablity. Unfortunately, the garbled circuit suffers from a one-time usage. The combination of fully homomorphic encryption (FHE) and garbled circuits enables the client and the server to reuse the garbled circuit with multiple inputs (Gennaro et al.). However, there still seems to be a long way to go for improving the efficiency of all known FHE schemes and it need much stronger security assumption. On the other hand, the construction is only proven to be secure in a weaker model where an adversary can not …

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Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau Feb 2019

Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We address the problem of maritime traffic management in busy waterways to increase the safety of navigation by reducing congestion. We model maritime traffic as a large multiagent systems with individual vessels as agents, and VTS authority as the regulatory agent. We develop a maritime traffic simulator based on historical traffic data that incorporates realistic domain constraints such as uncertain and asynchronous movement of vessels. We also develop a traffic coordination approach that provides speed recommendation to vessels in different zones. We exploit the nature of collective interactions among agents to develop a scalable policy gradient approach that can scale …

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Comparelda: A Topic Model For Document Comparison, Maksim Tkachenko, Hady Wirawan Lauw Feb 2019

Comparelda: A Topic Model For Document Comparison, Maksim Tkachenko, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

A number of real-world applications require comparison of entities based on their textual representations. In this work, we develop a topic model supervised by pairwise comparisons of documents. Such a model seeks to yield topics that help to differentiate entities along some dimension of interest, which may vary from one application to another. While previous supervised topic models consider document labels in an independent and pointwise manner, our proposed Comparative Latent Dirichlet Allocation (CompareLDA) learns predictive topic distributions that comply with the pairwise comparison observations. To fit the model, we derive a maximum likelihood estimation method via augmented variational approximation …

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Deception In Finitely Repeated Security Games, Thanh H. Nguyen, Yongzhao Wang, Arunesh Sinha, Michael P. Wellman Feb 2019

Deception In Finitely Repeated Security Games, Thanh H. Nguyen, Yongzhao Wang, Arunesh Sinha, Michael P. Wellman

Research Collection School Of Computing and Information Systems

Allocating resources to defend targets from attack is often complicated by uncertainty about the attacker’s capabilities, objectives, or other underlying characteristics. In a repeated interaction setting, the defender can collect attack data over time to reduce this uncertainty and learn an effective defense. However, a clever attacker can manipulate the attack data to mislead the defender, influencing the learning process toward its own benefit. We investigate strategic deception on the part of an attacker with private type information, who interacts repeatedly with a defender. We present a detailed computation and analysis of both players’ optimal strategies given the attacker may …

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Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau Feb 2019

Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We address the problem of maritime traffic management in busy waterways to increase the safety of navigation by reducing congestion. We model maritime traffic as a large multiagent systems with individual vessels as agents, and VTS authority as the regulatory agent. We develop a maritime traffic simulator based on historical traffic data that incorporates realistic domain constraints such as uncertain and asynchronous movement of vessels. We also develop a traffic coordination approach that provides speed recommendation to vessels in different zones. We exploit the nature of collective interactions among agents to develop a scalable policy gradient approach that can scale …

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Topical Co-Attention Networks For Hashtag Recommendation On Microblogs, Yang Li, Ting Liu, Jingwen Hu, Jing Jiang Feb 2019

Topical Co-Attention Networks For Hashtag Recommendation On Microblogs, Yang Li, Ting Liu, Jingwen Hu, Jing Jiang

Research Collection School Of Computing and Information Systems

Hashtags provide a simple and natural way of organizing content in microblog services. Along with the fast growing of microblog services, the task of recommending hashtags for microblogs has been given increasing attention in recent years. However, much of the research depends on hand-crafted features. Motivated by the successful use of neural models for many natural language processing tasks, in this paper, we adopt an attention based neural network to learn the representation of a microblog post. Unlike previous works, which only focus on content attention of microblogs, we propose a novel Topical CoAttention Network (TCAN) that jointly models content …

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Grand Challenges In Accessible Maps, Jon E. Froehlich, Anke M. Brock, Anat Caspi, Joao Guerreiro, Kotaro Hara, Reuben Kirkham, Johannes Schoning, Benjamin Tannert Feb 2019

Grand Challenges In Accessible Maps, Jon E. Froehlich, Anke M. Brock, Anat Caspi, Joao Guerreiro, Kotaro Hara, Reuben Kirkham, Johannes Schoning, Benjamin Tannert

Research Collection School Of Computing and Information Systems

In this forum we celebrate research that helps to successfully bring the benefits of computing technologies to children, older adults, people with disabilities, and other populations that are often ignored in the design of mass-marketed products.

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Manifold-Valued Image Generation With Wasserstein Generative Adversarial Nets, Zhiwu Huang, Wu J., G. L. Van Feb 2019

Manifold-Valued Image Generation With Wasserstein Generative Adversarial Nets, Zhiwu Huang, Wu J., G. L. Van

Research Collection School Of Computing and Information Systems

Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversarial Nets (WGANs), are studied on manifold-valued images that are frequently encountered in real-world applications. To fill the gap, this paper first formulates the problem of generating manifold-valued images and exploits three typical instances: hue-saturation-value (HSV) color image generation, chromaticity-brightness (CB) color image generation, and diffusion-tensor (DT) image generation. For the proposed generative modeling problem, we then introduce a theorem of optimal transport to derive a new Wasserstein distance of data distributions on complete manifolds, enabling us to …

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Stock Market Prediction Analysis By Incorporating Social And News Opinion And Sentiment, Zhaoxia Wang, Seng-Beng Ho, Zhiping Lin Feb 2019

Stock Market Prediction Analysis By Incorporating Social And News Opinion And Sentiment, Zhaoxia Wang, Seng-Beng Ho, Zhiping Lin

Research Collection School Of Computing and Information Systems

The price of the stocks is an important indicator for a company and many factors can affect their values. Different events may affect public sentiments and emotions differently, which may have an effect on the trend of stock market prices. Because of dependency on various factors, the stock prices are not static, but are instead dynamic, highly noisy and nonlinear time series data. Due to its great learning capability for solving the nonlinear time series prediction problems, machine learning has been applied to this research area. Learning-based methods for stock price prediction are very popular and a lot of enhanced …

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Evolutionary Trends In The Collaborative Review Process Of A Large Software System, Subhajit Datta, Poulami Sarkar Feb 2019

Evolutionary Trends In The Collaborative Review Process Of A Large Software System, Subhajit Datta, Poulami Sarkar

Research Collection School Of Computing and Information Systems

In this paper, we study the evolutionary trends in the collaborative review process of a large open source software system. As expected, the number of reviews, the number of reviews commented on, as well as the number of reviewers, and the interactions between them show increasing trends over time. But unexpectedly, levels of clustering between developers in their interaction networks show a decreasing trend, even as connections between them increase. In the context of our study, clustering is an indicator of developer collaboration, whereas connection points to how intensely developers work together. Thus the trends we observe can inform how …

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Risk Pooling, Supply Chain Hierarchy, And Analysts' Forecasts, Nan Hu, Jian-Yu Ke, Ling Liu, Yue Zhang Feb 2019

Risk Pooling, Supply Chain Hierarchy, And Analysts' Forecasts, Nan Hu, Jian-Yu Ke, Ling Liu, Yue Zhang

Research Collection School Of Computing and Information Systems

We investigate whether a firm's risk pooling affects its analysts' forecasts, specifically in terms of forecast accuracy and their use of public vs. private information, and how risk pooling interacts with a firm's position in the supply chain to affect analysts' forecasts. We use a social network analysis method to operationalize risk pooling and supply chain hierarchy, and find that risk pooling significantly reduces analysts' forecast errors and increases (decreases) their use of public (private) information. We also find that the positive (negative) relationships between risk pooling and analyst forecast accuracy and analysts' use of public (private) information are more …

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Multi-Authority Attribute-Based Keyword Search Over Encrypted Cloud Data, Yibin Miao, Robert H. Deng, Ximeng Liu, Kim-Kwang Raymond. Choo, Hongjun Wu, Hongwei Li Jan 2019

Multi-Authority Attribute-Based Keyword Search Over Encrypted Cloud Data, Yibin Miao, Robert H. Deng, Ximeng Liu, Kim-Kwang Raymond. Choo, Hongjun Wu, Hongwei Li

Research Collection School Of Computing and Information Systems

Searchable Encryption (SE) is an important technique to guarantee data security and usability in the cloud at the same time. Leveraging Ciphertext-Policy Attribute-Based Encryption (CP-ABE), the Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) scheme can achieve keyword-based retrieval and fine-grained access control simultaneously. However, the single attribute authority in existing CP-ABKS schemes is tasked with costly user certificate verification and secret key distribution. In addition, this results in a single-point performance bottleneck in distributed cloud systems. Thus, in this paper, we present a secure Multi-authority CP-ABKS (MABKS) system to address such limitations and minimize the computation and storage burden on resource-limited devices …

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Successor Features Based Multi-Agent Rl For Event-Based Decentralized Mdps, Tarun Gupta, Akshat Kumar, Praveen Paruchuri Jan 2019

Successor Features Based Multi-Agent Rl For Event-Based Decentralized Mdps, Tarun Gupta, Akshat Kumar, Praveen Paruchuri

Research Collection School Of Computing and Information Systems

Decentralized MDPs (Dec-MDPs) provide a rigorous framework for collaborative multi-agent sequential decisionmaking under uncertainty. However, their computational complexity limits the practical impact. To address this, we focus on a class of Dec-MDPs consisting of independent collaborating agents that are tied together through a global reward function that depends upon their entire histories of states and actions to accomplish joint tasks. To overcome scalability barrier, our main contributions are: (a) We propose a new actor-critic based Reinforcement Learning (RL) approach for event-based Dec-MDPs using successor features (SF) which is a value function representation that decouples the dynamics of the environment from …

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Dabke: Secure Deniable Attribute-Based Key Exchange Framework, Yangguang Tian, Yingjiu Li, Guomin Yang, Willy Susilo, Yi Mu, Hui Cui, Yinghui Zhang Jan 2019

Dabke: Secure Deniable Attribute-Based Key Exchange Framework, Yangguang Tian, Yingjiu Li, Guomin Yang, Willy Susilo, Yi Mu, Hui Cui, Yinghui Zhang

Research Collection School Of Computing and Information Systems

We introduce the first deniable attribute-based key exchange (DABKE) framework that is resilient to impersonation attacks. We define the formal security models for DABKE framework, and propose a generic compiler that converts any attribute-based key exchanges into deniable ones. We prove that it can achieve session key security and user privacy in the standard model, and strong deniability in the simulation-based paradigm. In particular, the proposed generic compiler ensures: 1) a dishonest user cannot impersonate other user's session participation in conversations since implicit authentication is used among authorized users; 2) an authorized user can plausibly deny his/her participation after secure …

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An Economic Analysis Of Consumer Learning On Entertainment Shopping Websites, Jin Li, Zhiling Guo, Geoffrey K.F. Tso Jan 2019

An Economic Analysis Of Consumer Learning On Entertainment Shopping Websites, Jin Li, Zhiling Guo, Geoffrey K.F. Tso

Research Collection School Of Computing and Information Systems

Online entertainment shopping, normally supported by the pay-to-bid auction mechanism, represents an innovative business model in e-commerce. Because the unique selling mechanism combines features of shopping and online auction, consumers expect both monetary return and entertainment value from their participation. We propose a dynamic structural model to analyze consumer behaviors on entertainment shopping websites. The model captures the consumer learning process, based both on individual participation experiences and also on observational learning of historical auction information. We estimate the model using a large data set from an online entertainment shopping website. Results show that consumers’ initial participation incentives mainly come …

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When Human Cognitive Modeling Meets Pins: User-Independent Inter-Keystroke Timing Attacks, Ximing Liu, Yingjiu Li, Robert H. Deng, Bing Chang, Shujun Li Jan 2019

When Human Cognitive Modeling Meets Pins: User-Independent Inter-Keystroke Timing Attacks, Ximing Liu, Yingjiu Li, Robert H. Deng, Bing Chang, Shujun Li

Research Collection School Of Computing and Information Systems

This paper proposes the first user-independent inter-keystroke timing attacks on PINs. Our attack method is based on an inter-keystroke timing dictionary built from a human cognitive model whose parameters can be determined by a small amount of training data on any users (not necessarily the target victims). Our attacks can thus be potentially launched on a large scale in real-world settings. We investigate inter-keystroke timing attacks in different online attack settings and evaluate their performance on PINs at different strength levels. Our experimental results show that the proposed attack performs significantly better than random guessing attacks. We further demonstrate that …

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Climate Risks And Market Efficiency, Harrison Hong, Frank Weikai Li, Jiangmin Xu Jan 2019

Climate Risks And Market Efficiency, Harrison Hong, Frank Weikai Li, Jiangmin Xu

Research Collection Lee Kong Chian School Of Business

Climate science finds that the trend towards higher global temperatures exacerbates the risks of droughts. We investigate whether the prices of food stocks efficiently discount these risks. Using data from thirty-one countries with publicly-traded food companies, we rank these countries each year based on their long-term trends toward droughts using the Palmer Drought Severity Index. A poor trend ranking for a country forecasts relatively poor profit growth for food companies in that country. It also forecasts relatively poor food stock returns in that country. This return predictability is consistent with food stock prices underreacting to climate change risks.

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Large Scale Online Multiple Kernel Regression With Application To Time-Series Prediction, Doyen Sahoo, Steven C. H. Hoi, Bin Lin Jan 2019

Large Scale Online Multiple Kernel Regression With Application To Time-Series Prediction, Doyen Sahoo, Steven C. H. Hoi, Bin Lin

Research Collection School Of Computing and Information Systems

Kernel-based regression represents an important family of learning techniques for solving challenging regression tasks with non-linear patterns. Despite being studied extensively, most of the existing work suffers from two major drawbacks as follows: (i) they are often designed for solving regression tasks in a batch learning setting, making them not only computationally inefficient and but also poorly scalable in real-world applications where data arrives sequentially; and (ii) they usually assume that a fixed kernel function is given prior to the learning task, which could result in poor performance if the chosen kernel is inappropriate. To overcome these drawbacks, this work …

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Driving And Effective Data-Ready Culture: How Companies Can Take On A Datadriven Approach To 11 Business, Johnson Poh Jan 2019

Driving And Effective Data-Ready Culture: How Companies Can Take On A Datadriven Approach To 11 Business, Johnson Poh

MITB Thought Leadership Series

TECHNOLOGY has turned the tables in favour of consumers, enabling them to find goods and services faster and access more choices. Companies now compete more intensely to capture consumers’ mindshare and scour for ways to keep their products relevant. But every coin has two sides. While technology has empowered consumers with choice, it has also offered companies a plethora of data to understand consumers better. This puts the odds in favour of companies that can leverage on data to gain consumer insights and meet their business objectives.

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Semi-Supervised Deep Embedded Clustering, Yazhou Ren, Kangrong Hu, Xinyi Dai, Lili Pan, Steven C. H. Hoi, Zenglin Xu Jan 2019

Semi-Supervised Deep Embedded Clustering, Yazhou Ren, Kangrong Hu, Xinyi Dai, Lili Pan, Steven C. H. Hoi, Zenglin Xu

Research Collection School Of Computing and Information Systems

Clustering is an important topic in machine learning and data mining. Recently, deep clustering, which learns feature representations for clustering tasks using deep neural networks, has attracted increasing attention for various clustering applications. Deep embedded clustering (DEC) is one of the state-of-theart deep clustering methods. However, DEC does not make use of prior knowledge to guide the learning process. In this paper, we propose a new scheme of semi-supervised deep embedded clustering (SDEC) to overcome this limitation. Concretely, SDEC learns feature representations that favor the clustering tasks and performs clustering assignments simultaneously. In contrast to DEC, SDEC incorporates pairwise constraints …

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Analysis Of Bus Ride Comfort Using Smartphone Sensor Data, Hoong-Chor Chin, Xingting Pang, Zhaoxia Wang Jan 2019

Analysis Of Bus Ride Comfort Using Smartphone Sensor Data, Hoong-Chor Chin, Xingting Pang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Passenger comfort is an important indicator that is often used to measure the quality of public transport services. It may also be a crucial factor in the passenger’s choice of transport mode. The typical method of assessing passenger comfort is through a passenger interview survey which can be tedious. This study aims to investigate the relationship between bus ride comfort based on ride smoothness and the vehicle’s motion detected by the smartphone sensors. An experiment was carried out on a bus fixed route within the University campus where comfort levels were rated on a 3-point scale and recorded at 5-second …

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Privacy-Preserving Attribute-Based Keyword Search In Shared Multi-Owner Setting, Yibin Miao, Ximeng Liu, Robert H. Deng, Robert H. Deng, Jjguo Li, Hongwei Li, Jianfeng Ma Jan 2019

Privacy-Preserving Attribute-Based Keyword Search In Shared Multi-Owner Setting, Yibin Miao, Ximeng Liu, Robert H. Deng, Robert H. Deng, Jjguo Li, Hongwei Li, Jianfeng Ma

Research Collection Yong Pung How School Of Law

Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) facilitates search queries and supports fine-grained access control over encrypted data in the cloud. However, prior CP-ABKS schemes were designed to support unshared multi-owner setting, and cannot be directly applied in the shared multi-owner setting (where each record is accredited by a fixed number of data owners), without incurring high computational and storage costs. In addition, due to privacy concerns on access policies, most existing schemes are vulnerable to off-line keyword-guessing attacks if the keyword space is of polynomial size. Furthermore, it is difficult to identify malicious users who leak the secret keys when more …

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Automatic Query Reformulation For Code Search Using Crowdsourced Knowledge, Mohammad M. Rahman, Chanchal K. Roy, David Lo Jan 2019

Automatic Query Reformulation For Code Search Using Crowdsourced Knowledge, Mohammad M. Rahman, Chanchal K. Roy, David Lo

Research Collection School Of Computing and Information Systems

Traditional code search engines (e.g., Krugle) often do not perform well with natural language queries. They mostly apply keyword matching between query and source code. Hence, they need carefully designed queries containing references to relevant APIs for the code search. Unfortunately, preparing an effective search query is not only challenging but also time-consuming for the developers according to existing studies. In this article, we propose a novel query reformulation technique–RACK–that suggests a list of relevant API classes for a natural language query intended for code search. Our technique offers such suggestions by exploiting keyword-API associations from the questions and answers …

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Automatic Query Reformulation For Code Search Using Crowdsourced Knowledge, Mohammad M. Rahman, Chanchal K. Roy, David Lo Jan 2019

Automatic Query Reformulation For Code Search Using Crowdsourced Knowledge, Mohammad M. Rahman, Chanchal K. Roy, David Lo

Research Collection School Of Computing and Information Systems

Traditional code search engines (e.g., Krugle) often do not perform well with natural language queries. They mostly apply keyword matching between query and source code. Hence, they need carefully designed queries containing references to relevant APIs for the code search. Unfortunately, preparing an effective search query is not only challenging but also time-consuming for the developers according to existing studies. In this article, we propose a novel query reformulation technique–RACK–that suggests a list of relevant API classes for a natural language query intended for code search. Our technique offers such suggestions by exploiting keyword-API associations from the questions and answers …

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Vireo @ Video Browser Showdown 2019, Phuong Anh Nguyen, Chong-Wah Ngo, Danny Francis, Benoit Huet Jan 2019

Vireo @ Video Browser Showdown 2019, Phuong Anh Nguyen, Chong-Wah Ngo, Danny Francis, Benoit Huet

Research Collection School Of Computing and Information Systems

In this paper, the VIREO team video retrieval tool is described in details. As learned from Video Browser Showdown (VBS) 2018, the visualization of video frames is a critical need to improve the browsing effectiveness. Based on this observation, a hierarchical structure that represents the video frame clusters has been built automatically using k-means and self-organizing-map and used for visualization. Also, the relevance feedback module which relies on real-time supportvector-machine classification becomes unfeasible with the large dataset provided in VBS 2019 and has been replaced by a browsing module with pre-calculated nearest neighbors. The preliminary user study results on IACC.3 …

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Global Inference For Aspect And Opinion Terms Co-Extraction Based On Multi-Task Neural Networks, Jianfei Yu, Jing Jiang, Rui Xia Jan 2019

Global Inference For Aspect And Opinion Terms Co-Extraction Based On Multi-Task Neural Networks, Jianfei Yu, Jing Jiang, Rui Xia

Research Collection School Of Computing and Information Systems

Extracting aspect terms and opinion terms are two fundamental tasks in opinion mining. The recent success of deep learning has inspired various neural network architectures, which have been shown to achieve highly competitive performance in these two tasks. However, most existing methods fail to explicitly consider the syntactic relations among aspect terms and opinion terms, which may lead to the inconsistencies between the model predictions and the syntactic constraints. To this end, we first apply a multi-task learning framework to implicitly capture the relations between the two tasks, and then propose a global inference method by explicitly modelling several syntactic …

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A State Aggregation Approach For Stochastic Multiperiod Last-Mile Ride-Sharing Problems, Lucas Agussurja, Shih-Fen Cheng, Hoong Chuin Lau Jan 2019

A State Aggregation Approach For Stochastic Multiperiod Last-Mile Ride-Sharing Problems, Lucas Agussurja, Shih-Fen Cheng, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

The arrangement of last-mile services is playing an increasingly important role in making public transport more accessible. We study the use of ridesharing in satisfying last-mile demands with the assumption that demands are uncertain and come in batches. The most important contribution of our paper is a two-level Markov decision process framework that is capable of generating a vehicle-dispatching policy for the aforementioned service. We introduce state summarization, representative states, and sample-based cost estimation as major approximation techniques in making our approach scalable. We show that our approach converges and solution quality improves as sample size increases. We also apply …

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A First Look At Unfollowing Behavior On Github, Jing Jiang, David Lo, Yun Yang, Jianfeng Li, Li Zhang Jan 2019

A First Look At Unfollowing Behavior On Github, Jing Jiang, David Lo, Yun Yang, Jianfeng Li, Li Zhang

Research Collection School Of Computing and Information Systems

Many open source software projects rely on contributors to fix bugs and contribute new features. On GitHub, developers often broadcast their activities to followers, which may entice followers to be project contributors. It is important to understand unfollowing behavior, maintain current followers, and attract some followers to become contributors in OSS projects.Our objective in this paper is to provide a comprehensive analysis of unfollowing behavior on GitHub. To the best of our knowledge, we present a first look at unfollowing behavior on GitHub. We collect a dataset containing 701,364 developers and their 4,602,440 following relationships in March 2016. We also …

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Modeling Location-Based Social Network Data With Area Attraction And Neighborhood Competition, Thanh Nam Doan, Ee-Peng Lim Jan 2019

Modeling Location-Based Social Network Data With Area Attraction And Neighborhood Competition, Thanh Nam Doan, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Modeling user check-in behavior helps us gain useful insights about venues as well as the users visiting them. These insights are important in urban planning and recommender system applications. Since check-in behavior is the result of multiple factors, this paper focuses on studying two venue related factors, namely, area attraction and neighborhood competition. The former refers to the ability of a spatial area covering multiple venues to collectively attract check-ins from users, while the latter represents the extent to which a venue can compete with other venues in the same area for check-ins. We first embark on empirical studies to …

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