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Web Information Systems Engineering – WISE 2016

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Cover of 'Web Information Systems Engineering – WISE 2016'

Table of Contents

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    Book Overview
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    Chapter 1 Attribute-Based Influence Maximization in Social Networks
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    Chapter 2 Twitter Normalization via 1-to-N Recovering
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    Chapter 3 A Data Cleaning Method for CiteSeer Dataset
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    Chapter 4 Towards Understanding URL Resources in Recent Sina Weibo
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    Chapter 5 Nonparametric Bayesian Probabilistic Latent Factor Model for Group Recommender Systems
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    Chapter 6 Joint User Knowledge and Matrix Factorization for Recommender Systems
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    Chapter 7 GEMRec: A Graph-Based Emotion-Aware Music Recommendation Approach
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    Chapter 8 Domain Dictionary-Based Topic Modeling for Social Text
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    Chapter 9 Towards an Impact-Driven Quality Control Model for Imbalanced Crowdsourcing Tasks
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    Chapter 10 Modeling and Analyzing Engagement in Social Network Challenges
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    Chapter 11 Select, Link and Rank: Diversified Query Expansion and Entity Ranking Using Wikipedia
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    Chapter 12 Multi-dimension Diversification in Legal Information Retrieval
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    Chapter 13 Generating Multiple Diverse Summaries
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    Chapter 14 Diversifying the Results of Keyword Queries on Linked Data
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    Chapter 15 Semantic Similarity of Workflow Traces with Various Granularities
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    Chapter 16 Intermediate Semantics Based Distance Metric Learning for Video Annotation and Similarity Measurements
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    Chapter 17 A Community Detection Algorithm Considering Edge Betweenness and Vertex Similarity
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    Chapter 18 Measuring and Ensuring Similarity of User Interfaces: The Impact of Web Layout
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    Chapter 19 Semantic Context-Aware Recommendation via Topic Models Leveraging Linked Open Data
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    Chapter 20 Optimizing Factorization Machines for Top-N Context-Aware Recommendations
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    Chapter 21 Taxonomy Tree Based Similarity Measurement of Textual Attributes of Items for Recommender Systems
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    Chapter 22 A Personalized Recommendation Algorithm for User-Preference Similarity Through the Semantic Analysis
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    Chapter 23 Learning-Based SPARQL Query Performance Prediction
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    Chapter 24 Can Online Emotions Predict the Stock Market in China?
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    Chapter 25 Predicting Replacement of Smartphones with Mobile App Usage
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    Chapter 26 Real Time Prediction on Revisitation Behaviors of Short-Term Type Commodities
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    Chapter 27 Parallel Materialization of Datalog Programs with Spark for Scalable Reasoning
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    Chapter 28 A Data Type-Driven Property Alignment Framework for Product Duplicate Detection on the Web
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    Chapter 29 A Semantic Data Parallel Query Method Based on Hadoop
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    Chapter 30 A Strategy to Improve Accuracy of Multi-dimensional Feature Forecasting in Big Data Stream Computing Environments
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    Chapter 31 Automatic Creation and Analysis of a Linked Data Cloud Diagram
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    Chapter 32 Fast Multi-keywords Search over Encrypted Cloud Data
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    Chapter 33 A Scalable Parallel Semantic Reasoning Algorithm-Based on RDFS Rules on Hadoop
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    Chapter 34 Cloud Resource Allocation from the User Perspective: A Bare-Bones Reinforcement Learning Approach
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    Chapter 35 Event Phase Extraction and Summarization
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    Chapter 36 ESAP: A Novel Approach for Cross-Platform Event Dissemination Trend Analysis Between Social Network and Search Engine
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    Chapter 37 Learning Event Profile for Improving First Story Detection in Twitter Stream
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    Chapter 38 A Novel Approach of Discovering Local Community Using Node Vector Model
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    Chapter 39 Labeled Phrase Latent Dirichlet Allocation
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    Chapter 40 WTEN: An Advanced Coupled Tensor Factorization Strategy for Learning from Imbalanced Data
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    Chapter 41 Mining Actionable Knowledge Using Reordering Based Diversified Actionable Decision Trees
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    Chapter 42 Improving Distant Supervision of Relation Extraction with Unsupervised Methods
Overall attention for this book and its chapters
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Title
Web Information Systems Engineering – WISE 2016
Published by
Springer International Publishing, January 2016
DOI 10.1007/978-3-319-48740-3
ISBNs
978-3-31-948739-7, 978-3-31-948740-3
Editors

Wojciech Cellary, Mohamed F. Mokbel, Jianmin Wang, Hua Wang, Rui Zhou, Yanchun Zhang

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X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 17%
Student > Master 5 14%
Student > Doctoral Student 3 9%
Student > Postgraduate 2 6%
Researcher 1 3%
Other 1 3%
Unknown 17 49%
Readers by discipline Count As %
Computer Science 11 31%
Engineering 3 9%
Earth and Planetary Sciences 1 3%
Mathematics 1 3%
Social Sciences 1 3%
Other 1 3%
Unknown 17 49%