Titlebook: Web-Age Information Management; WAIM 2016 Internatio Shaoxu Song,Yongxin Tong Conference proceedings 2016 Springer International Publishing

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0302-9743 otemporal Data Management and Mining for the Web (SDMMW 2016)..? The International Workshop on Semi-structured Big Data Management and Applications (SemiBDMA 2016)...? The International Workshop on Mobile Web Data Analytics (MWDA2016).978-3-319-47120-4978-3-319-47121-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Modeling User Preference from Rating Data Based on the Bayesian Network with a Latent Variablent variable to describe user preference and Bayesian network (BN) with a latent variable as the framework for representing the relationships among the observed and the latent variables, and define user preference BN (abbreviated as UPBN). To construct UPBN effectively, we first give the property and

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A Hybrid Approach for Sparse Data Classification Based on Topic Modelfforts have been devoted to improve the efficiency of normal text classification. However, it is still immature in terms of high-dimension and sparse data processing. In this paper, we present a new method which fancifully utilizes Biterm Topic Model (BTM) and Support Vector Machine (SVM). By using

沉默

Human Activity Recognition in a Smart Home Environment with Stacked Denoising Autoencodersent nature of human activities is characterized by a high degree of complexity and uncertainty, it poses a great challenge to build a robust activity recognition model. This study aims to exploit deep learning techniques to learn high-level features from the binary sensor data under the assumption t

Critical

Modeling User Preference from Rating Data Based on the Bayesian Network with a Latent Variablent variable to describe user preference and Bayesian network (BN) with a latent variable as the framework for representing the relationships among the observed and the latent variables, and define user preference BN (abbreviated as UPBN). To construct UPBN effectively, we first give the property and

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Ranking Online Services by Aggregating Ordinal Preferencesnking mechanism is an important service for e-commerce that facilitates consumers’ decision-making process. Traditional services ranking methods ignore the fact that customers cannot rate services under the same criteria, which leads to the ratings are actually incomparable. In this paper, we propos

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DroidDelver: An Android Malware Detection System Using Deep Belief Network Based on API Call Blocksmportant topic in cyber security. Currently, the major defense against Android malware is commercial mobile security products which mainly use signature-based method for detection. However, attackers can easily devise methods, such as obfuscation and repackaging, to evade the detection, which calls

有花

Human Activity Recognition in a Smart Home Environment with Stacked Denoising Autoencodersent nature of human activities is characterized by a high degree of complexity and uncertainty, it poses a great challenge to build a robust activity recognition model. This study aims to exploit deep learning techniques to learn high-level features from the binary sensor data under the assumption t