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26th International World Wide Web Conference, WWW 2017 Companion

作者： Zhao, Shenglin Zhao, Tong King, Irwin Lyu, Michael R. Shenzhen Key Laboratory of Rich Media Big Data Analytics Application Shenzhen Research Institute Chinese University of Hong Kong Nanshan District Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong

ISBN: (纸本)9781450349147

Point-of-interest (POI) recommendation is an important application for location-based social networks (LBSNs), which learns the user preference and mobility pattern from check-in sequences to recommend POIs. Previous studies show that modeling the sequential pattern of user check-ins is necessary for POI recommendation. Markov chain model, recurrent neural network, and the word2vec framework are used to model check-in sequences in previous work. However, all previous sequential models ignore the fact that check-in sequences on different days naturally exhibit the various temporal characteristics, for instance, "work" on weekday and "entertainment" on weekend. In this paper, we take this challenge and propose a Geo-Temporal sequential embedding rank (Geo-Teaser) model for POI recommendation. Inspired by the success of the word2vec framework to model the sequential contexts, we propose a temporal POI embedding model to learn POI representations under some particular temporal state. The temporal POI embedding model captures the contextual check-in information in sequences and the various temporal characteristics on different days as well. Furthermore, We propose a new way to incorporate the geographical influence into the pairwise preference ranking method through discriminating the unvisited POIs according to geographical information. Then we develop a geographically hierarchical pairwise preference ranking model. Finally, we propose a unified framework to recommend POIs combining these two models. To verify the effectiveness of our proposed method, we conduct experiments on two real-life datasets. Experimental results show that the Geo-Teaser model outperforms state-of-the-art models. Compared with the best baseline competitor, the Geo-Teaser model improves at least 20% on both datasets for all metrics. © 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.

关键词： Location based services

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PERSONALIZED SEQUENTIAL CHECK-IN PREDICTION: BEYOND GEOGRAPHICAL AND TEMPORAL CONTEXTS

PERSONALIZED SEQUENTIAL CHECK-IN PREDICTION: BEYOND GEOGRAPH...

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IEEE International Conference on Multimedia and Expo

作者： Shenglin Zhao Xixian Chen Irwin King Michael R. Lyu Shenzhen Key Laboratory of Rich Media Big Data Analytics and Application The Chinese University of Hong Kong Shenzhen China

Check-in prediction is an important task for location-based systems, which maps a noisy estimate of a user's current location to a semantically meaningful point-of-interest (POI), such as a restaurant or store. In this paper, we leverage the personalized preference and sequential check-in pattern to improve the traditional methods that base on the geographical and temporal contexts. In our approach, we propose a Gaussian mixture model and a histogram distribution estimation model to learn the contextual features from relevant spatial and temporal information, respectively. Furthermore, we employ user and POI embeddings to model the personalized preference and leverage a stacked Long-Short Term Memory (LSTM) model to learn the sequential check-in pattern. Combining the contextual features and the personalized sequential patterns together, we propose a wide and deep neural network for the check-in prediction task. Experimental evaluations on two real-life datasets demonstrate that our proposed method outperforms state-of-the-art models.

关键词： Deep Learning Recurrent Neural Network Location-based Services Check-in Prediction

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Modeling the homophily effect between links and communities for overlapping community detection 25

Modeling the homophily effect between links and communities ...

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Zhang, Hongyi Zhao, Tong King, Irwin Lyu, Michael R. Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong

Overlapping community detection has drawn much attention recently since it allows nodes in a network to have multiple community memberships. A standard framework to deal with overlapping community detection is Matrix Factorization (MF). Although all existing MF-based approaches use links as input to identify communities, the relationship between links and communities is still under-investigated. Most of the approaches only view links as consequences of communities (community-to-link) but fail to explore how nodes' community memberships can be represented by their linked neighbors (link-to-community). In this paper, we propose a Homophily-based Nonnegative Matrix Factorization (HNMF) to model both-sided relationships between links and communities. From the community-to-link perspective, we apply a preference-based pairwise function by assuming that nodes with common communities have a higher probability to build links than those without common communities. From the link-to-community perspective, we propose a new community representation learning with network embedding by assuming that linked nodes have similar community representations. We conduct experiments on several real-world networks and the results show that our HNMF model is able to find communities with better quality compared with state-of-the-art baselines.

关键词： Matrix factorization

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Online Non-negative Dictionary Learning via Moment Information for Sparse Poisson Coding

Online Non-negative Dictionary Learning via Moment Informati...

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International Joint Conference on Neural Networks

作者： Xiaotian Yu Haiqin Yang Irwin King Michael R. Lyu Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute The Chinese University of Hong Kong China

ISBN: (纸本)9781509006212

Online dictionary learning for sparse coding is an effective tool for data analysis. It incrementally learns a set of basis vectors with sparse linear combinations of these vectors when new samples appear. Previous work assumes that the samples embed Gaussian noises, which weaken the power of these methods in handling real applications with non-negative data (e.g., frequency data in word counts). Differently, in this paper, we concentrate on online learning for non-negative dictionary by using moment information for sparse Poisson coding. We exploit the non-negativity of Poisson models to learn a set of non-negative basis vectors and a non-negative sparse linear combination for the moment information of samples. Specifically, we first formulate the online learning problem via the maximum-a-posteriori (MAP) framework. We then propose a novel online algorithm which alternatively updates the sparse-coefficient vector and the basis vectors with non-negativity constraints when a new sample arrives. More importantly, we present sufficient convergence analyses to guarantee the performance of the proposed algorithm, which leads to convergence of a stable dictionary for characterizing the moment information of samples. We finally conduct a series of experiments on word-counts data and image data to show merits of the proposed online algorithm.

关键词： Dictionaries Algorithm design and analysis Encoding Image coding Convergence Optimization Gaussian distribution

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Distributed Information-Theoretic Metric Learning in Apache Spark

Distributed Information-Theoretic Metric Learning in Apache ...

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International Joint Conference on Neural Networks

作者： Yuxin Su Haiqin Yang Irwin King Michael Lyu Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute The Chinese University of Hong Kong China

ISBN: (纸本)9781509006212

Distance metric learning (DML) is an effective similarity learning tool to learn a distance function from examples to enhance the model performance in applications of classification, regression, and ranking, etc. Most DML algorithms need to learn a Mahalanobis matrix, a positive semidefinite matrix that scales quadratically with the number of dimensions of input data. This brings huge computational cost in the learning procedure, and makes all proposed algorithms infeasible for extremely high-dimensional data even with the low-rank approximation. Differently, in this paper, we take advantage of the power of parallel computation and propose a novel distributed distance metric learning algorithm based on a state-of-the-art DML algorithm, Information-Theoretic Metric Learning (ITML). More specifically, we utilize the property that each positive semidefinite matrix can be decomposed into a combination of rank-one and trace-one matrices and convert the original sequential training procedure into a parallel one. In most cases, the communication demands of the proposed method are also reduced from O(d~2) to O(cd), where d is the number of dimensions of the data and c is the number of constraints in DML and can be smaller than d by appropriate selection. Moreover importantly, we present a rigorous theoretical analysis to upper bound the Bregman divergence between the sequential algorithm and the parallel algorithm, which guarantees the correctness and performance of the proposed algorithm. Our experiments on datasets with O(10~5) features demonstrate the competitive scalability and the performance compared with the original ITML algorithm.

关键词： Matrix decomposition Measurement Covariance matrices Approximation algorithms Eigenvalues and eigenfunctions Machine learning algorithms Algorithm design and analysis

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Towards continuous and passive authentication via touch biometrics: an experimental study on smartphones 14

Towards continuous and passive authentication via touch biom...

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Proceedings of the Tenth USENIX Conference on Usable Privacy and Security

作者： Hui Xu Yangfan Zhou Michael R. Lyu Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute The Chinese University of Hong Kong and MoE Key Laboratory of High Confidence Software Technologies (CUHK Sub-Lab) Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute The Chinese University of Hong Kong and Dept. of Computer Science & Engineering The Chinese University of Hong Kong

ISBN: (纸本)9781931971133

Current smartphones generally cannot continuously authenticate users during runtime. This poses severe security and privacy threats: A malicious user can manipulate the phone if bypassing the screen lock. To solve this problem, our work adopts a continuous and passive authentication mechanism based on a user's touch operations on the touchscreen. Such a mechanism is suitable for smartphones, as it requires no extra hardware or intrusive user interface. We study how to model multiple types of touch data and perform continuous authentication accordingly. As a first attempt, we also investigate the fundamentals of touch operations as biometrics by justifying their distinctiveness and permanence. A one-month experiment is conducted involving over 30 users. Our experiment results verify that touch biometrics can serve as a promising method for continuous and passive authentication.

关键词： smartphone touch biometrics continuous authentication

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Exploiting game theoretic analysis for link recommendation in social networks 15

Exploiting game theoretic analysis for link recommendation i...

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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Zhao, Tong Zhao, H. Vicky King, Irwin Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong Electrical and Computer Engineering Department University of Alberta EdmontonAB Canada

ISBN: (纸本)9781450337946

The popularity of Online Social Networks (OSNs) has attracted great research interests in different fields. In Economics, researchers use game theory to analyze the mechanism of network formation, which is called Network Formation Game. While in Computer Science, much effort has been done in building machine learning models to predict future or missing links. However, there are few works considering how to combine game theoretic analysis and machine learning models. Therefore, in this paper, we study the problem of Exploiting Game Theoretic Analysis for Link Recommendation in Social Networks. Our goal is to improve link recommendation accuracy via leveraging the power of Network Formation Games into machine learning models. We present two different approaches to solve this problem. First, we propose a three-phase method that straightforwardly combines game theoretic analysis with machine learning models. Second, we develop a unified model, BPRLGT, that incorporates Network Formation Game into a Bayesian ranking framework for link recommendation. Specifically, BPRLGT takes advantage of network topology and we design a game theoretic sampling approach to improve its training process. The experiments are conducted on four real world datasets and the results on all datasets demonstrate that both our proposed three-phase method and the unified ranking model outperform the baseline methods. © 2015 ACM.

关键词： Game theory

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Leveraging social connections to improve personalized ranking for collaborative filtering 14

Leveraging social connections to improve personalized rankin...

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23rd ACM International Conference on Information and Knowledge Management, CIKM 2014

作者： Zhao, Tong McAuley, Julian King, Irwin Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong Department of Computer Science and Engineering UC San Diego San DiegoCA United States

ISBN: (纸本)9781450325981

Recommending products to users means estimating their preferences for certain items over others. This can be cast either as a problem of estimating the rating that each user will give to each item, or as a problem of estimating users' relative preferences in the form of a ranking. Although collaborative-filtering approaches can be used to identify users who rate and rank products similarly, another source of data that informs us about users' preferences is their set of social connections. Both rating- and ranking-based paradigms are important in real-world recommendation settings, though rankings are especially important in settings where explicit feedback in the form of a numerical rating may not be available. Although many existing works have studied how social connections can be used to build better models for rating prediction, few have used social connections as a means to derive more accurate ranking-based models. Using social connections to better estimate users' rankings of products is the task we consider in this paper. We develop a model, SBPR (Social Bayesian Personalized Ranking), based on the simple observation that users tend to assign higher ranks to items that their friends prefer. We perform experiments on four real-world recommendation data sets, and show that SBPR outperforms alternatives in ranking prediction both in warm- and cold-start settings. Copyright 2014 ACM.

关键词： Collaborative filtering

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Improving latent factor models via personalized feature projection for one class recommendation 15

Improving latent factor models via personalized feature proj...

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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

ISBN: (纸本)9781450337946

Latent Factor models, which transform both users and items into the same latent feature space, are one of the most successful and ubiquitous models in recommender systems. Most existing models in this paradigm define both users' and items' latent factors to be of the same size and use an inner product to represent a user's 'compatibility' with an item. Intuitively, users' factors encode 'preferences' while item factors encode 'properties', so that the inner product encodes how well an item matches a user's preferences. However, a user's opinion of an item may be more complex, for example each dimension of each user's opinion may depend on a combination of multiple item factors simultaneously. Thus it may be better to view each dimension of a user's preference as a personalized projection of an item's properties so that the preference model can capture complex relationships between items' properties and users' preferences. Therefore, in this paper we propose a novel personalized feature projection method to model users' preferences over items. Specifically, for each user, we define a personalized projection matrix, which takes the place of user-specific factors from existing models. This matrix describes a mapping between items' factors and users' preferences in order to build personalized preference models for each user and item. The proposed personalized feature projection method is quite general and existing latent factor models, for example, can be cast as a special case. We present three objective functions to optimize predictions in the form of ranked lists of users' preferences over items, and demonstrate how each can be used to improve one-class recommendation performance. Experiments are conducted on four real-world datasets and our results show that our personalized feature projection method outperforms several state-of-the-art methods on various evaluation metrics. © 2015 ACM.

关键词： Collaborative filtering

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Title-guided encoding for keyphrase generation

arXiv

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arXiv 2018年

作者： Chen, Wang Gao, Yifan Zhang, Jiani King, Irwin Lyu, Michael R. Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong Shenzhen Key Laboratory of Rich Media Big Data Analytics and Application Shenzhen Research Institute Chinese University of Hong Kong Shenzhen China

keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP). Most previous methods solve this problem in an extractive manner, while recently, several attempts are made under the generative setting using deep neural networks. However, the state-of-the-art generative methods simply treat the document title and the document main body equally, ignoring the leading role of the title to the overall document. To solve this problem, we introduce a new model called Title-Guided Network (TG-Net) for automatic keyphrase generation task based on the encoderdecoder architecture with two new features: (i) the title is additionally employed as a query-like input, and (ii) a titleguided encoder gathers the relevant information from the title to each word in the document. Experiments on a range of KG datasets demonstrate that our model outperforms the state-of-the-art models with a large margin, especially for documents with either very low or very high title length ratios. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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