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Articles 2191 - 2220 of 3560

Full-Text Articles in Databases and Information Systems

Travel Recommendation Via Author Topic Model Based Collaborative Filtering, Shuhui Jiang, Xueming Qian, Jialie Shen, Tao Mei Jan 2015

Travel Recommendation Via Author Topic Model Based Collaborative Filtering, Shuhui Jiang, Xueming Qian, Jialie Shen, Tao Mei

Research Collection School Of Computing and Information Systems

While automatic travel recommendation has attracted a lot of attentions, the existing approaches generally suffer from different kinds of weaknesses. For example, sparsity problem can significantly degrade the performance of traditional collaborative filtering (CF). If a user only visits very few locations, accurate similar user identification becomes very challenging due to lack of sufficient information. Motivated by this concern, we propose an Author Topic Collaborative Filtering (ATCF) method to facilitate comprehensive Points of Interest (POIs) recommendation for social media users. In our approach, the topics about user preference (e.g., cultural, cityscape, or landmark) are extracted from the textual description of …

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An Adaptive Gradient Method For Online Auc Maximization, Yi Ding, Peilin Zhao, Steven C. H. Hoi, Yew-Soon Ong Jan 2015

An Adaptive Gradient Method For Online Auc Maximization, Yi Ding, Peilin Zhao, Steven C. H. Hoi, Yew-Soon Ong

Research Collection School Of Computing and Information Systems

Learning for maximizing AUC performance is an important research problem in machine learning. Unlike traditional batch learning methods for maximizing AUC which often suffer from poor scalability, recent years have witnessed some emerging studies that attempt to maximize AUC by single-pass online learning approaches. Despite their encouraging results reported, the existing online AUC maximization algorithms often adopt simple stochastic gradient descent approaches, which fail to exploit the geometry knowledge of the data observed in the online learning process, and thus could suffer from relatively slow convergence. To overcome the limitation of the existing studies, in this paper, we propose a …

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Semi-Universal Portfolios With Transaction Costs, Dingjiang Huang, Yan Zhu, Bin Li, Shuigeng Zhou, Steven C. H. Hoi Jan 2015

Semi-Universal Portfolios With Transaction Costs, Dingjiang Huang, Yan Zhu, Bin Li, Shuigeng Zhou, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Online portfolio selection (PS) has been extensively studied in artificial intelligence and machine learning communities in recent years. An important practical issue of online PS is transaction cost, which is unavoidable and nontrivial in real financial trading markets. Most existing strategies, such as universal portfolio (UP) based strategies, often rebalance their target portfolio vectors at every investment period, and thus the total transaction cost increases rapidly and the final cumulative wealth degrades severely. To overcome the limitation, in this paper we investigate new investment strategies that rebalances its portfolio only at some selected instants. Specifically, we design a novel on-line …

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Are Features Equally Representative? A Feature-Centric Recommendation, Chenyi Zhang, Ke Wang, Ee-Peng Lim, Qinneng Xu, Jianling Sun, Hongkun Yu Jan 2015

Are Features Equally Representative? A Feature-Centric Recommendation, Chenyi Zhang, Ke Wang, Ee-Peng Lim, Qinneng Xu, Jianling Sun, Hongkun Yu

Research Collection School Of Computing and Information Systems

Typically a user prefers an item (e.g., a movie) because she likes certain features of the item (e.g., director, genre, producer). This observation motivates us to consider a feature-centric recommendation approach to item recommendation: instead of directly predicting the rating on items, we predict the rating on the features of items, and use such ratings to derive the rating on an item. This approach offers several advantages over the traditional item-centric approach: it incorporates more information about why a user chooses an item, it generalizes better due to the denser feature rating data, it explains the prediction of item ratings …

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Mining User Viewpoints In Online Discussions, Minghui Qiu Jan 2015

Mining User Viewpoints In Online Discussions, Minghui Qiu

Dissertations and Theses Collection (Open Access)

Online discussion forums are a type of social media which contains rich usercontributed facts, opinions, and user interactions on diverse topics. The large volume of opinionated data generated in online discussions provides an ideal testbed for user opinion mining. In particular, mining user opinions on social and political issues from online discussions is useful not only to government organizations and companies but also to social and political scientists. In this dissertation, we propose to study the task of mining user viewpoints or stances from online discussions on social and political issues. Specifically, we will talk about our proposed approaches for …

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Push Or Pull? A Website's Strategic Choice Of Content Delivery Mechanism, Dan Ma Jan 2015

Push Or Pull? A Website's Strategic Choice Of Content Delivery Mechanism, Dan Ma

Research Collection School Of Computing and Information Systems

Really simple syndication (RSS) technology enables an alternative delivery mechanism for online content. Instead of waiting passively for users to pull online content out, websites can push it to potential users through RSS. This is expected to significantly affect user behavior, website profitability, and market equilibrium. This research uses an economic model to study the impact of RSS adoption and examine whether it increases a website’s profit and competitive advantage. The findings are intriguing: they demonstrate that RSS can either increase or decrease website profit. In a competitive context, RSS adoption can actually be a disadvantage; in some cases, it …

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From Cells To Streets: Estimating Mobile Paths With Cellular-Side Data, Qatar Computing Research Institute, University Of Birmingham, Seattle University Of Washington, Haewoon Kwak Dec 2014

From Cells To Streets: Estimating Mobile Paths With Cellular-Side Data, Qatar Computing Research Institute, University Of Birmingham, Seattle University Of Washington, Haewoon Kwak

Research Collection School Of Computing and Information Systems

Through their normal operation, cellular networks are a repository of continuous location information from their subscribed devices. Such information, however, comes at a coarse granularity both in terms of space, as well as time. For otherwise inactive devices, location information can be obtained at the granularity of the associated cellular sector, and at infrequent points in time, that are sensitive to the structure of the network itself, and the level of mobility of the device. In this paper, we are asking the question of whether such sparse information can help to identify the paths followed by mobile connected devices throughout …

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Towards Intelligent Caring Agents For Aging-In-Place: Issues And Challenges, Di Wang, Budhitama Subagdja, Yilin Kang, Ah-Hwee Tan Dec 2014

Towards Intelligent Caring Agents For Aging-In-Place: Issues And Challenges, Di Wang, Budhitama Subagdja, Yilin Kang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

The aging of the world’s population presents vast societal and individual challenges. The relatively shrinking workforce to support the growing population of the elderly leads to a rapidly increasing amount of technological innovations in the field of elderly care. In this paper, we present an integrated framework consisting of various intelligent agents with their own expertise and responsibilities working in a holistic manner to assist, care, and accompany the elderly around the clock in the home environment. To support the independence of the elderly for Aging-In-Place (AIP), the intelligent agents must well understand the elderly, be fully aware of the …

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Probabilistic Latent Document Network Embedding, Tuan M. V. Le, Hady W. Lauw Dec 2014

Probabilistic Latent Document Network Embedding, Tuan M. V. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

A document network refers to a data type that can be represented as a graph of vertices, where each vertex is associated with a text document. Examples of such a data type include hyperlinked Web pages, academic publications with citations, and user profiles in social networks. Such data have very high-dimensional representations, in terms of text as well as network connectivity. In this paper, we study the problem of embedding, or finding a low-dimensional representation of a document network that "preserves" the data as much as possible. These embedded representations are useful for various applications driven by dimensionality reduction, such …

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Detecting Flow Anomalies In Distributed Systems, Freddy Chong-Tat Chua, Ee Peng Lim, Bernardo Huberman Dec 2014

Detecting Flow Anomalies In Distributed Systems, Freddy Chong-Tat Chua, Ee Peng Lim, Bernardo Huberman

Research Collection School Of Computing and Information Systems

Deep within the networks of distributed systems, one often finds anomalies that affect their efficiency and performance. These anomalies are difficult to detect because the distributed systems may not have sufficient sensors to monitor the flow of traffic within the interconnected nodes of the networks. Without early detection and making corrections, these anomalies may aggravate over time and could possibly cause disastrous outcomes in the system in the unforeseeable future. Using only coarse-grained information from the two end points of network flows, we propose a network transmission model and a localization algorithm, to detect the location of anomalies and rank …

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Mydeal: A Mobile Shopping Assistant Matching User Preferences To Promotions, Kartik Muralidharan, Swapna Gottipati, Jing Jiang, Narayan Ramasubbu, Rajesh Krishna Balan Dec 2014

Mydeal: A Mobile Shopping Assistant Matching User Preferences To Promotions, Kartik Muralidharan, Swapna Gottipati, Jing Jiang, Narayan Ramasubbu, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

A common problem in large urban cities is the huge number of retail options available. In response, a number of shopping assistance applications have been created for mobile phones. However, these applications mostly allow users to know where stores are or find promotions on specific items. What is missing is a system that factors in a user's shopping preferences and automatically tells them which stores are of their interest. The key challenge in this system is twofold; 1) building a matching algorithm that can combine user preferences with fairly unstructured deals and store information to generate a final rank ordered …

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Android Or Ios For Better Privacy Protection?, Jin Han, Qiang Yan, Debin Gao, Jianying Zhou, Huijie Robert Deng Dec 2014

Android Or Ios For Better Privacy Protection?, Jin Han, Qiang Yan, Debin Gao, Jianying Zhou, Huijie Robert Deng

Research Collection School Of Computing and Information Systems

With the rapid growth of the mobile market, security of mobile platforms is receiving increasing attention from both research community as well as the public. In this paper, we make the first attempt to establish a baseline for security comparison between the two most popular mobile platforms. We investigate applications that run on both Android and iOS and examine the difference in the usage of their security sensitive APIs (SS-APIs). Our analysis over 2,600 applications shows that iOS applications consistently access more SS-APIs than their counterparts on Android. The additional privileges gained on iOS are often associated with accessing private …

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Extracting Interest Tags From Twitter User Biographies, Ying Ding, Jing Jiang Dec 2014

Extracting Interest Tags From Twitter User Biographies, Ying Ding, Jing Jiang

Research Collection School Of Computing and Information Systems

Twitter, one of the most popular social media platforms, has been studied from different angles. One of the important sources of information in Twitter is users’ biographies, which are short self-introductions written by users in free form. Biographies often describe users’ background and interests. However, to the best of our knowledge, there has not been much work trying to extract information from Twitter biographies. In this work, we study how to extract information revealing users’ personal interests from Twitter biographies. A sequential labeling model is trained with automatically constructed labeled data. The popular patterns expressing user interests are extracted and …

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High-Dimensional Data Stream Classification Via Sparse Online Learning, Dayong Wang, Pengcheng Wu, Peilin Zhao, Yue Wu, Chunyan Miao, Steven C. H. Hoi Dec 2014

High-Dimensional Data Stream Classification Via Sparse Online Learning, Dayong Wang, Pengcheng Wu, Peilin Zhao, Yue Wu, Chunyan Miao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

The amount of data in our society has been exploding in the era of big data today. In this paper, we address several open challenges of big data stream classification, including high volume, high velocity, high dimensionality, and high sparsity. Many existing studies in data mining literature solve data stream classification tasks in a batch learning setting, which suffers from poor efficiency and scalability when dealing with big data. To overcome the limitations, this paper investigates an online learning framework for big data stream classification tasks. Unlike some existing online data stream classification techniques that are often based on first-order …

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Data Preparation For Social Network Mining And Analysis, Yazhe Wang Dec 2014

Data Preparation For Social Network Mining And Analysis, Yazhe Wang

Dissertations and Theses Collection (Open Access)

This dissertation studies the problem of preparing good-quality social network data for data analysis and mining. Modern online social networks such as Twitter, Facebook, and LinkedIn have rapidly grown in popularity. The consequent availability of a wealth of social network data provides an unprecedented opportunity for data analysis and mining researchers to determine useful and actionable information in a wide variety of fields such as social sciences, marketing, management, and security. However, raw social network data are vast, noisy, distributed, and sensitive in nature, which challenge data mining and analysis tasks in storage, efficiency, accuracy, etc. Many mining algorithms cannot …

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Celelabel: An Interactive System For Annotating Celebrities In Web Videos, Zhineng Chen, Jinfeng Bai, Chong-Wah Ngo, Bailan Feng, Bo Xu Nov 2014

Celelabel: An Interactive System For Annotating Celebrities In Web Videos, Zhineng Chen, Jinfeng Bai, Chong-Wah Ngo, Bailan Feng, Bo Xu

Research Collection School Of Computing and Information Systems

Manual annotation of celebrities in Web videos is an essential task in many people-related Web services. The task, however, poses a significant challenge even to skillful annotators, mainly due to the large quantity of unfamiliar and greatly varied celebrities, and the lack of a customized system for it. This work develops CeleLabel, an interactive system for manually annotating celebrities in the Web video domain. The peculiarity of CeleLabel is to exploit and display multiple types of information that could assist the annotation, including video content, context surrounding and within a video, celebrity images on the Web, and human factors. Using …

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Scalable Visual Instance Mining With Threads Of Features, Wei Zhang, Hongzhi Li, Chong-Wah Ngo, Shih-Fu Chang Nov 2014

Scalable Visual Instance Mining With Threads Of Features, Wei Zhang, Hongzhi Li, Chong-Wah Ngo, Shih-Fu Chang

Research Collection School Of Computing and Information Systems

We address the problem of visual instance mining, which is to extract frequently appearing visual instances automatically from a multimedia collection. We propose a scalable mining method by exploiting Thread of Features (ToF). Specifically, ToF, a compact representation that links consistent features across images, is extracted to reduce noises, discover patterns, and speed up processing. Various instances, especially small ones, can be discovered by exploiting correlated ToFs. Our approach is significantly more effective than other methods in mining small instances. At the same time, it is also more efficient by requiring much fewer hash tables. We compared with several state-of-the-art …

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Click-Through-Based Subspace Learning For Image Search, Yingwei Pan, Ting Yao, Xinmei Tian, Houqiang Li, Chong-Wah Ngo Nov 2014

Click-Through-Based Subspace Learning For Image Search, Yingwei Pan, Ting Yao, Xinmei Tian, Houqiang Li, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

One of the fundamental problems in image search is to rank image documents according to a given textual query. We address two limitations of the existing image search engines in this paper. First, there is no straightforward way of comparing textual keywords with visual image content. Image search engines therefore highly depend on the surrounding texts, which are often noisy or too few to accurately describe the image content. Second, ranking functions are trained on query-image pairs labeled by human labelers, making the annotation intellectually expensive and thus cannot be scaled up. We demonstrate that the above two fundamental challenges …

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An Ecological Model For Digital Platforms Maintenance And Evolution, Paolo Rocchi, Paolo Spagnoletti, Subhajit Datta Nov 2014

An Ecological Model For Digital Platforms Maintenance And Evolution, Paolo Rocchi, Paolo Spagnoletti, Subhajit Datta

Research Collection School Of Computing and Information Systems

The maintenance of software products has been studied extensively in both software engineering and management information systems. Such studies are mainly focused on the activities that take place prior to starting the maintenance phase. Their contribution is either related to the improvement of software quality or to validating contingency models for reducing maintenance efforts. The continuous maintenance philosophy suggests to shift the attention within the maintenance phase for better coping with the evolutionary trajectories of digital platforms. In this paper, we examine the maintenance process of a digital platform from the perspective of the software vendor. Based on our empirical …

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Combining Multiple Kernel Methods On Riemannian Manifold For Emotion Recognition In The Wild, M. Liu, R. Wang, S. Li, S. Shan, Zhiwu Huang, X. Chen Nov 2014

Combining Multiple Kernel Methods On Riemannian Manifold For Emotion Recognition In The Wild, M. Liu, R. Wang, S. Li, S. Shan, Zhiwu Huang, X. Chen

Research Collection School Of Computing and Information Systems

In this paper, we present the method for our submission to the Emotion Recognition in the Wild Challenge (EmotiW 2014). The challenge is to automatically classify the emotions acted by human subjects in video clips under realworld environment. In our method, each video clip can be represented by three types of image set models (i.e. linear subspace, covariance matrix, and Gaussian distribution) respectively, which can all be viewed as points residing on some Riemannian manifolds. Then different Riemannian kernels are employed on these set models correspondingly for similarity/distance measurement. For classification, three types of classifiers, i.e. kernel SVM, logistic regression, …

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Hybrid Euclidean-And-Riemannian Metric Learning For Image Set Classification, Zhiwu Huang, R. Wang, S. Shan, X. Chen Nov 2014

Hybrid Euclidean-And-Riemannian Metric Learning For Image Set Classification, Zhiwu Huang, R. Wang, S. Shan, X. Chen

Research Collection School Of Computing and Information Systems

We propose a novel hybrid metric learning approach to combine multiple heterogenous statistics for robust image set classification. Specifically, we represent each set with multiple statistics – mean, covariance matrix and Gaussian distribution, which generally complement each other for set modeling. However, it is not trivial to fuse them since the mean vector with dd-dimension often lies in Euclidean space RdRd, whereas the covariance matrix typically resides on Riemannian manifold Sym+dSymd+. Besides, according to information geometry, the space of Gaussian distribution can be embedded into another Riemannian manifold Sym+d+1Symd+1+. To fuse these statistics from heterogeneous spaces, we propose a Hybrid …

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Modloc: Localizing Multiple Objects In Dynamic Indoor Environment, Xiaonan Guo, Dian Zhang, Kaishun Wu, Lionel M. Ni Nov 2014

Modloc: Localizing Multiple Objects In Dynamic Indoor Environment, Xiaonan Guo, Dian Zhang, Kaishun Wu, Lionel M. Ni

Research Collection School Of Computing and Information Systems

Radio frequency (RF) based technologies play an important role in indoor localization, since Radio Signal Strength (RSS) can be easily measured by various wireless devices without additional cost. Among these, radio map based technologies (also referred as fingerprinting technologies) are attractive due to high accuracy and easy deployment. However, these technologies have not been extensively applied on real environment for two fatal limitations. First, it is hard to localize multiple objects. When the number of target objects is unknown, constructing a radio map of multiple objects is almost impossible. Second, environment changes will generate different multipath signals and severely disturb …

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Band Selection For Hyperspectral Images Using Probabilistic Memetic Algorithm, Liang Feng, Ah-Hwee Tan, Meng-Hiot Lim, Si Wei Jiang Nov 2014

Band Selection For Hyperspectral Images Using Probabilistic Memetic Algorithm, Liang Feng, Ah-Hwee Tan, Meng-Hiot Lim, Si Wei Jiang

Research Collection School Of Computing and Information Systems

Band selection plays an important role in identifying the most useful and valuable information contained in the hyperspectral images for further data analysis such as classification, clustering, etc. Memetic algorithm (MA), among other metaheuristic search methods, has been shown to achieve competitive performances in solving the NP-hard band selection problem. In this paper, we propose a formal probabilistic memetic algorithm for band selection, which is able to adaptively control the degree of global exploration against local exploitation as the search progresses. To verify the effectiveness of the proposed probabilistic mechanism, empirical studies conducted on five well-known hyperspectral images against two …

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Perspectives On Task Ownership In Mobile Operating System Development [Invited Talk], Subhajit Datta Nov 2014

Perspectives On Task Ownership In Mobile Operating System Development [Invited Talk], Subhajit Datta

Research Collection School Of Computing and Information Systems

There can be little contention about Stroustrup's epigrammatic remark: our civilization runs on software. However a caveat is increasingly due, much of the software that runs our civilization, runs on mobile devices today. Mobile operating systems have come to play a preeminent role in the ubiquity and utility of such devices. The development ecosystem of Android - one of the most popular mobile operating systems - presents an interesting context for studying whether and how collaboration dynamics in mobile development differ from conventional software development. In this paper, we examine factors that influence task ownership in Android development. Our results …

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Developer Involvement Considered Harmful? An Empirical Examination Of Android Bug Resolution Times, Subhajit Datta, Proshanta Sarkar, Subhashis Majumder Nov 2014

Developer Involvement Considered Harmful? An Empirical Examination Of Android Bug Resolution Times, Subhajit Datta, Proshanta Sarkar, Subhashis Majumder

Research Collection School Of Computing and Information Systems

In large scale software development ecosystems, there is a common perception that higher developer involvement leads to faster resolution of bugs. This is based on conjectures around more ``eyeballs" making bugs "shallow" -- whose validity and applicability are not without dispute. In this paper, we posit that the level of developer attention as well as its extent of diversity influence how quickly bugs get resolved. We report results from a study of 1,000+ Android bugs. We find statistically significant evidence that attention and diversity have contrasting relationships with the resolution time of bugs, even after controlling for factors such as …

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The Evolution Of Research On Multimedia Travel Guide Search And Recommender Systems, Junge Shen, Zhiyong Cheng, Jialie Shen, Tao Mei, Xinbo Gao Nov 2014

The Evolution Of Research On Multimedia Travel Guide Search And Recommender Systems, Junge Shen, Zhiyong Cheng, Jialie Shen, Tao Mei, Xinbo Gao

Research Collection School Of Computing and Information Systems

The importance of multimedia travel guide search and recommender systems has led to a substantial amount of research spanning different computer science and information system disciplines in recent years. The five core research streams we identify here incorporate a few multimedia computing and information retrieval problems that relate to the alternative perspectives of algorithm design for optimizing search/recommendation quality and different methodological paradigms to assess system performance at large scale. They include (1) query analysis, (2) diversification based on different criteria, (3) ranking and reranking, (4) personalization and (5) evaluation. Based on a comprehensive discussion and analysis of these streams, …

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Online Transfer Learning, Peilin Zhao, Steven C. H. Hoi, Jialei Wang, Bin Li Nov 2014

Online Transfer Learning, Peilin Zhao, Steven C. H. Hoi, Jialei Wang, Bin Li

Research Collection School Of Computing and Information Systems

This paper investigates a new machine learning framework of Online Transfer Learning (OTL), which aims to attack an online learning task on a target domain by transferring knowledge from some source domain. We do not assume data in the target domain follows the same distribution as that in the source domain, and the motivation of our work is to enhance a supervised online learning task on a target domain by exploiting the existing knowledge that had been learnt from training data in source domains. OTL is in general a challenging problem since data in both source and target domains not …

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Deep Learning For Content-Based Image Retrieval: A Comprehensive Study, Ji Wan, Dayong Wang, Steven C. H. Hoi, Pengcheng Wu, Jianke Zhu, Yongdong Zhang, Jintao Li Nov 2014

Deep Learning For Content-Based Image Retrieval: A Comprehensive Study, Ji Wan, Dayong Wang, Steven C. H. Hoi, Pengcheng Wu, Jianke Zhu, Yongdong Zhang, Jintao Li

Research Collection School Of Computing and Information Systems

Learning effective feature representations and similarity measures are crucial to the retrieval performance of a content-based image retrieval (CBIR) system. Despite extensive research efforts for decades, it remains one of the most challenging open problems that considerably hinders the successes of real-world CBIR systems. The key challenge has been attributed to the well-known "semantic gap" issue that exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human. Among various techniques, machine learning has been actively investigated as a possible direction to bridge the semantic gap in the long term. Inspired by recent successes of deep …

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Historical Traffic-Tolerant Paths In Road Networks, Pui Hang Li, Man Lung Yiu, Kyriakos Mouratidis Nov 2014

Historical Traffic-Tolerant Paths In Road Networks, Pui Hang Li, Man Lung Yiu, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

Historical traffic information is valuable for transportation analysis and planning, as well as for route search services. In view of these applications, we propose the k traffic-tolerant paths problem (TTP) on road networks, which takes a source-destination pair and historical traffic information as input, and returns k paths that minimize the aggregate (historical) travel time. Unlike the shortest path problem, the TTP problem has a combinatorial search space that renders the optimal solution expensive to compute. We propose an exact algorithm and a heuristic algorithm for this problem. Experiments on real traffic data demonstrate the effectiveness of TTP paths and …

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Dynamic Clustering Of Contextual Multi-Armed Bandits, Trong T. Nguyen, Hady W. Lauw Nov 2014

Dynamic Clustering Of Contextual Multi-Armed Bandits, Trong T. Nguyen, Hady W. Lauw

Research Collection School Of Computing and Information Systems

With the prevalence of the Web and social media, users increasingly express their preferences online. In learning these preferences, recommender systems need to balance the trade-off between exploitation, by providing users with more of the "same", and exploration, by providing users with something "new" so as to expand the systems' knowledge. Multi-armed bandit (MAB) is a framework to balance this trade-off. Most of the previous work in MAB either models a single bandit for the whole population, or one bandit for each user. We propose an algorithm to divide the population of users into multiple clusters, and to customize the …

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