This is my summary on the conference of WISE 2013 (International Conference on Web Information System Engineering )

1. Summary of WISE 2013
(13th
~15th
Oct. 2013, Nanjing)
07/11/14 1Middleware, CCNT, ZJU
Yueshen Xu
xyshzjucs@zju.edu.cn

2. Overview
 Introduction
 The 14th
International Conference on Web Information System
Engineering (WISE)
 13th
~ 15th
, Nanjing, China
 Before: HK, Kyoto, Singapore, Roma, Brisbane, NY, Nancy,
Poznan, etc.
 Statistics of acceptance
 Num. of Research papers: 48
 Accepted rate: 24%
 Num. of Long papers: 25; Num. of Short papers: 23
 10 Demos, 5 challenge reports
 Come from: 38 Countries around the world
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3. Overview
 General Co-chairs
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 PC Co-chairs
Yahho!
Research Lab
Victoria Univesity University of New
South Wales
Aristotle University AT&T Lab
 Industry Chairs
Google
Research
HKUST
 Tutorial Co-chairs
CUHK Poznan
University

4. Overview
 Publicity Co-chairs
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 Society Representative
Aristotle
University
University of
New South Wales
University of
Queensland
 Keynote Speaker
Peking University
Academician
Towards web-based
video processing
UCSB, ACM Fellow
Data-driven Methodologies
for understanding, managing
and analyzing Online Social
Networks

5. Overview
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 Keynote Speaker
University of Technology,
Sydney, Australia
Senior Member, IEEE
Big Data Related
Research Issues and
Progress
New Jersey Institute of
Technology
Security of Cyber-Physical
Systems
 Distinguished Young Scientists Forum on Big Data
 Jianmin Wang, Tsinghua Univ.
 Enhong Chen, USTC
 Aoying Zhou, East China normal Univ.
 Guoren Wang, Northeastern Univ.
 Etc.

6. Session
 Web Mining (2): 11
 Web Recommendation (2): 9
 Hidden Web: 4
 Web Services: 4
 Semi-structured Data and Modeling: 7
 Social Web (2) : 11
 Web Monitoring and Management: 6
 Innovative Techniques and Creations (2): 8
 Web Text Mining: 6
 Networks and Graphs: 6
 Demo (2): 5
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7. Web Mining(I)
 Ying Xu, Zhiqiang Gao, Campbell Wilson, Zhizheng Zhang, Man Zhu, Qiu Ji:
Entity Correspondence with Second-Order Markov Logic. 1-14
 Youliang Zhong, Lan Du, Jian Yang: Learning Social Relationship Strength
via Matrix Co-Factorization with Multiple Kernels. 15-28
 Shengsheng Shi, Wu Wei, Yulong Liu, Haitao Wang, Lei Luo, Chunfeng
Yuan, Yihua Huang: NEXIR: A Novel Web Extraction Rule Language toward
a Three-Stage Web Data Extraction Model. 29-42
 Jun Deng, Liang Du, Yi-Dong Shen: Heterogeneous Metric Learning for
Cross-Modal Multimedia Retrieval. 43-56
 Margarita Karkali, François Rousseau, Alexandros Ntoulas, Michalis
Vazirgiannis: Efficient Online Novelty Detection in News Online. 57-71
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In this paper we propose a KPMCF model to learn social relationship strength based on users’ latent
features inferred from both profile and interaction information. The proposed model takes an
uniformed approach of integrating Matrix Co-Factorization with Multiple Kernels. We conduct
experiments on real-world data sets for typical web mining applications, showing that the proposed
model produces better relationship strength measurement in comparison with other social factors.
In this paper, we propose a Bayesian personalized ranking based heterogeneous metric learning
(BPRHML) algorithm, which optimizes for correctly ranking the retrieval results. It uses pairwise
preference constraints as training data and explicitly optimizes for preserving these constraints. To
further encourage the smoothness of learning results, we integrate graph regularization with
Bayesian personalized ranking
In this paper, we propose a new novelty detection algorithm based on the Inverse Document
Frequency (IDF) scoring function. Computing novelty based on IDF enables us to avoid similarity
comparisons with previous documents in the text online, thus leading to faster execution times. At the
same time, our proposed approach outperforms several commonly used baselines when applied on a
real-world news articles dataset.
Eric Xing
CMU
Yueting Zhuang, YanFei Wang, Fei Wu, Yin Zhang, Weiming Lu: Supervised
Coupled Dictionary Learning with Group Structures for Multi-modal
Retrieval. AAAI 2013, Regular Paper
Deng Cai, Xiaofei He, Jiawei Han, Thomas S. Huang: Graph Regularized
Nonnegative Matrix Factorization for Data Representation. IEEE Trans. Pattern
Anal. Mach. Intell. 33(8): 1548-1560 (2011)

8. Web Mining(II)
 Daling Wang, Shi Feng, Dong Wang, Ge Yu: Detecting Opinion Drift from
Chinese Web Comments Based on Sentiment Distribution Computing.
72-81
 Peng Zhao, Xue Li, Ke Wang: Feature Extraction from Micro-blogs for
Comparison of Products and Services. 82-91
 Shahida Jabeen, Xiaoying Gao, Peter Andreae: Directional Context Helps:
Guiding Semantic Relatedness Computation by Asymmetric Word
Associations. 92-101
 Jun Hou, Richi Nayak: The Heterogeneous Cluster Ensemble Method
Using Hubness for Clustering Text Documents. 102-110
 Abdul Wahid, Xiaoying Gao, Peter Andreae: Exploiting User Queries for
Search Result Clustering. 111-120
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The proposed approach first determines possible drift timestamps according to the change of
comment number, computes different sentiment orientations and their distributions at these
timestamps, detects opinion drift according to the distribution changes, and analyzes the
influences of related events occurring in the timestamps. Extensive experiments were conducted in a
real comment set of Chinese forum.In this paper, we show our system namely OpinionAnalyzer, a novel social network analyzer designed
to collect opinions from Twitter micro-blogs about two given similar products for an effective
comparison between them. The system outcome is a structure of features for the given products that
people have expressed opinions about. Then the corresponding sentiment analysis on those features
is performed. Our system can be used to understand user’s preference to a certain product and show
the reasons why users prefer this product.
We propose a cluster ensemble method to map the corpus documents into the semantic space
embedded in Wikipedia and group them using multiple types of feature space. A heterogeneous
cluster ensemble is constructed with multiple types of relations i.e. document-term, document-
concept and document-category. A final clustering solution is obtained by exploiting associations
between document pairs and hubness of the documents
Adaboost & Bagging
George
Mason

9. Web Recommendation(I)
 Xin Liu: Towards Context-Aware Social Recommendation via Trust
Networks. 121-134
 Weilong Yao, Jing He, Guangyan Huang, Jie Cao, Yanchun
Zhang: Personalized Recommendation on Multi-Layer Context
Graph. 135-148
 Giseli Rabello Lopes, Luiz André P. Paes Leme, Bernardo Pereira
Nunes, Marco Antonio Casanova, Stefan Dietze: Recommending Tripleset
Interlinking through a Social Network Approach. 149-161
 Chong Wang, Yao Shen, Huan Yang, Minyi Guo: Improving Rocchio
Algorithm for Updating User Profile in Recommender Systems. 162-174
 Kai Wang, Richong Zhang, Xudong Liu, Xiaohui Guo, Hailong Sun, Jinpeng
Huai: Time-Aware Recommendation based on Tensor Factorization. 175-
188
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We employ random walk to collect the most relevant ratings based on the multi-dimensional
trustworthiness of users in the trust network. Factorization machines model is then applied on the
collected ratings to predict missing ratings considering various evaluation based on a real dataset
demonstrates that our approach improves the accuracy of the state-of-the-art social, context-aware
and trust-aware recommendation modelsIn this paper, we propose a Multi-Layer Context Graph (MLCG) model which incorporates a variety
of contextual information into a recommendation process and models the interactions between users
and items for better recommendation. Moreover, we provide a new ranking algorithm based on
Personalized PageRank for recommendation in MLCG, which captures users’ preferences and
current situations.  Top-K Recommendation
In this paper, we exploit a 3-way tensor to integrate context information. Based on this model, we
propose a time-aware recommendation approach. In addition, a tensor factorization-based
approach by maximizing the ranking performance measure is proposed for predicting the possible
temporal-spatial correlations.
SVM
Supervised v.s.
Unsupervised

10. Web Recommendation(II)
 Fangfang Li, Guandong Xu, Longbing Cao, Xiaozhong Fan, Zhendong Niu:
CGMF: Coupled Group-Based Matrix Factorization for Recommender
System. 189-198
 Zhengang Wu, Liangwen Yu, Huiping Sun, Zhi Guan, Zhong Chen:
Authenticating Users of Recommender Systems Using Naive Bayes. 199-
208
 Junyang Rao, Aixia Jia, Yansong Feng, Dongyan Zhao: Taxonomy Based
Personalized News Recommendation: Novelty and Diversity. 209-218
 Xiaochi Wei, Heyan Huang, Xin Xin, Xianxiang Yang: Distinguishing Social
Ties in Recommender Systems by Graph-Based Algorithms. 219-228
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In this paper, we propose an innovative coupled group-based matrix factorization model for
recommender system by leveraging the user and item groups learned by topic modeling and
incorporating couplings between users and items and within users and items.
Given a recommendation list, we improve a user’s satisfaction by introducing the taxonomy based
novelty and diversity metrics to include novel, but potentially related items into the list, and filter out
redundant ones. The experimental results show that the coarse grained knowledge resources can
help a content-based news recommender system provides accurate as well as user-oriented
recommendations. ::: Case Study
In this paper, we investigate the issue of distinguishing different users’ influence power in
recommendation systematically. We propose to employ three graph-based algorithms (including
PageRank, HITS, and heat diffusion) to distinguish and propagate the influence among the friends of
an active user, and then integrate them into the factorization-based social recommendation
framework.
Tomoharu Iwata, Amar Shah, Zoubin Ghahramani: Discovering latent influence in
online social activities via shared cascade poisson processes. 266-274, SIGKDD,
2013

11. Social Web (I)
 Nguyen Quoc Viet Hung, Nguyen Thanh Tam, Lam Ngoc Tran. An
Evaluation of Aggregation Techniques in Crowdsourcing, pp, 1-15
 Zhunchen Luo, jintao Tang and Ting Wang. Propagated Opinion Retrieval
in Twitter
 Meiling Wang, Xiang Zhou, Qiuming Tao, Wei Wu. Diversifying Tag
Selection Result for Tag Clouds by Enhancing both Coverage and
Dissimilarity
 Zhiang Wu, Alfredo Cuzzocrea. Community Detection in Multi-relational
Socail Networks
 Maria Giatsoglou, Despoina Chatzakou. Community Detection in Social
Networks by Leveraging Interactions and intensities
 Hemank Lamba and Ramasuri Narayanam. A Novel and Model
Independent Approach for Efficient Influence Maximization in Social
Networks
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We attempt to address this challenge by introducing a novel co-ranking framework,
named MutuRank. It makes full use of the mutual influence between relations and actors to transform
the multi-relational network to the single-relational network. We then present GMM-NK (Gaussian
Mixture Model with Neighbor Knowledge) based on local consistency principle to enhance the
performance of spectral clustering process in discovering overlapping communities.
In this paper we present a community detection approach for user interaction networks which
exploits both their structural properties and intensity patterns. The proposed approach builds on
existing graph clustering methods that identify both communities of nodes, as well as outliers. The
importance of incorporating interactions’ intensity in the community detection algorithm is initially
investigated by a benchmarking process on synthetic graphs.
In this paper, we precisely address this problem by proposing a new framework which fuses both link
and interaction data to come up with a backbone for a given social network, which can further be
used for efficient influence maximization. We then conduct thorough experimentation with several real
life social network datasets such as DBLP, Epinions, Digg, and Slashdot
Tomoharu Iwata, Amar Shah, Zoubin Ghahramani: Discovering latent influence in
online social activities via shared cascade poisson processes. 266-274, SIGKDD,
2013

12. Social Web (II)
 Lijiang Chen, Yibing Zhao, Shimin Chen. Personalized List Recommenda-
tion in Twitter, pp 88-103
 John Pfaltz. The Irreducible Spine of Undirected Networks
 Fotios Psallidas, Alexandros Ntoulas. Soc Web: Efficient Monitoring of
Social Network Acivities, pp 118-136
 Xiang Wang, Lele Yu, and Bin Cui. A multiple Feature Integration Model
to infer occupation from Social Media Records, pp 137-150
 Jinpeng Chen, Zhenyu Wu, etc. Recommending Interesting Landmarks
Based on Geo-tags from Photo Sharing Sites, pp 151-159
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To address the challenge of bootstrapping Twitter Lists, we envision a novel tool that automatically
creates personalized Twitter Lists and recommends them to users. Compared with lists created by
real Twitter users, the lists generated by our algorithms achieve 73.6% similarity.  Demo
In this paper, we propose a comprehensive framework to infer user’s occupation from his/her social
activities recorded in micro-blog message streams. A multi-source integrated classification model
is set up with some fine selected features. We first identify some beneficial basic content features,
and then we proceed to tailor a community discovery based latent dimension solution to extract
community features.
By using DFCM, we can cluster a large-scale geo-tagged web photo collection into groups (or
landmarks) by location. And then, we provide more friendly and comprehensive overviews for each
landmark. Subsequently, we model the users’ dynamical behaviors using the fusion user similarity,
which not only captures the overview semantic similarity, but also extract the trajectory similarity and
the landmark trajectory similarity.
Social Media/
Video SearchBei Pan, Yu Zheng, David Wilkie and Cyrus Shahabi. Crowd Sensing of Traffic
Anomalies based on Human Mobility and Social Media. SIGSPATIAL, 2013.
Jing Yuan, Yu Zheng, Xing Xie. Discovering regions of different functions in a city using
human mobility and POIs. SIGKDD, 2012

13. Web Text Mining
 Seema Nagar, Kanika Narang, Sameep Mehta, L. V. Subramaniam, Kuntal
Dey. Topical Discussions on unstructured Microblogs: Analysis from a
Geographical Perspective, pp. 160-173
 Lili Yang, Chunping Li, etc. Discovering Correlated Entities from News
Archives, pp. 174-187
 Min Peng, Jiajia Huang, etc. High Quality Microblog Extraction Based on
Multiple Features Fusion and Time Frequency Transformation, pp. 188-
201
 David S. Batista, Rui Silva, Bruno Martins, etc. A Minwise Hashing Method
for Addressing Relationship Extraction from Text, pp. 216-230
 Roberto Rodriguez, Victor m.Pavon, Dernando Macias, etc. Generating a
Conceptual Representation of a Legacy Web Application, pp. 231-240
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we identify and characterize topical discussions at different geographical granularities, such as
countries and cities. We observe geographical localization of evolution of topical discussions.
Experimental results suggest that these discussion threads tend to evolve more strongly over
geographically finer granularities: they evolve more at city levels compared to country levels, and
more at country levels compared to globally.
We propose an extraction framework to get high quality information by considering different features
globally in social media. Specially, in order to reduce computing time and improve extraction
precision, some important social media features are employed and transformed into wavelet domain
and fused further, to get a weighted ensemble value. A large scale of Sina microblog dataset is used
to evaluate the framework’s performance.

14. Networks and Graphs
 Shanshan Huang and Xiaojun Wan. AKMiner: Domain-Specific Knowledge
Graph Mining from Academic Literatures, pp. 241-255
 Dayong Ye and minjie Zhang. A Study on the Evolution of Cooperation in
Networks. pp 285-298
 Natwar Modani, Kuntal Dey, Ritesh Gupta, Shantanu Godbole. CDR
Analysis Based Telco Churn Prediction and Customer Behavior Insights:
A Case Study, pp 256-269
 Helan Liang, Yanhua Du, Sujian Li. An Improved Genetic Algorithm for
Service Selection under Temporal Constraints in Cloud Computing, pp.
309-318
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In this paper, we propose a novel system called AKMiner (Academic Knowledge Miner) to
automatically mine useful knowledge from the articles in a specific domain, and then visually
present the knowledge graph to users. Our system consists of two major components: a) the
extraction module which extracts academic concepts and relations jointly based on Markov Logic
Network, and b) the visualization module which generates knowledge graphs, including concept-
cloud graphs and concept relation graphs.
In this paper, a self-organisation based strategy is proposed for the evolution of cooperation in
networks, which can utilise the strengths of current strategies and avoid the limitations of current
strategies. The proposed strategy is empirically evaluated and its good performance is exhibited.
Moreover, we also theoretically find that, in static networks, the final proportion of cooperators evolved
by any pure strategies fluctuates cyclically irrespective of the initial proportion of cooperators.
In this case study paper, we present our experience of participating in a competitive evaluation for
churn prediction and customer insights for a leading Asian telecom operator. We build a data mining
model to predict churners using key performance indicators (KPI) based on customer Call Detail
Records (CDR) and additional customer data available with the operator. Further, we analyze the
social network formed between the (prepaid and postpaid) churners as well as the entire subscriber
base. ::: Case Study

15. Thank You !
Q&A
Thank You !
Q&A
07/11/14 15Middleware, CCNT, ZJU
Summary of WISE 2013