# CQARank: Jointly Model Topics and Expertise in Community Question Answering

Save this PDF as:

Size: px
Start display at page:

## Transcription

4 The generative process of Q&A posts of users can be described as follows: For the u-th user, (u = 1, 2,, U) Draw a user specific topic distribution θ u Dir(α) For the e-th expertise, (e = 1, 2,, E) Draw an expertise specific vote distribution N (µ e, Σ e) N G(α 0, β 0, µ 0, k 0) For the k-th topic, (k = 1, 2,, K) Draw a topic specific tag distribution ψ k Dir(η) Draw a topic specific word distribution ϕ k Dir(γ) For the u-th user, (u = 1, 2,, U) - Draw a user topical expertise distribution φ k,u Dir(β) For the u-th user (u = 1, 2,, U) For the n-th post (n = 1, 2,, N u) - Draw topic z Multi(θ u) - Draw expertise e Multi(φ z,u) - Draw vote v N (µ e, Σ e) For the l-th word (l = 1,, L u,n) - Draw word w Multi(ϕ z) For the p-th tag (p = 1,, P u,n) - Draw tag t Multi(ψ z) 3.2 Learning and Parameter Estimation We use collapsed Gibbs sampling to obtain samples of the hidden variable assignment and estimate the model parameters of TEM. The Gibbs Sampling process is described in Algorithm 1. Algorithm 1 Gibbs Sampling for TEM. 1: procedure GIBBSSAMPLING 2: Initialize Z and E by assigning random values 3: for each Gibbs Sampling iteration do 4: for each user u = 1,, U do 5: for u s n-th QA post, n = 1,, N u do 6: Let c denotes {u, n} 7: Update (µ ec, c, Σ ec, c ) according to Eqn. 4 8: Draw z c and e c according to Eqn. 1 9: Update (µ ec, Σ ec ) according to Eqn. 4 10: end for 11: end for 12: end for 13: Estimate model parameters θ, ψ, φ and ϕ 14: end procedure We jointly sample topic z u,n and expertise e u,n for each user u and post n, where we assume (µ, Σ) for all the expertise levels are known. Let c denotes {u, n}, Θ denotes all the Dirichlet priors and Normal-Gamma priors, we can drive the Gibbs update rule for z u,n and e u,n as follows: = = p(z c = z, e c = e Z c, W, E c, V, T, Θ) p(z, W, E, V, T Θ) p(z c, W, E c, V, T Θ) (C k u + α) (C k u, c + α) (C w z + γ) (C w z, c + γ) (Cz,u e + β) N (vc µe, Σe) (Cz,u, e c + β) C z u, c + α K k=1 Ck u, c + Kα T t=1 n t c n t c p=1 (Ct z, c + η + p 1) (C t z + η) (C t z, c + η) V n w c w=1 i=1 (Cw z, c + γ + i 1) n w c V j=1 w=1 (Cw z, c + V γ + j 1) T q=1 t=1 (Ct z, c + T η + q 1) Cz,u, e c + β E N (vc µe, Σe), (1) e=1 Ce z,u, c + Eβ where ( ) is a Dirichlet delta function" which can be seen as a multidimensional extension to beta function [14], N ( ) is Gaussian distribution. To estimate parameters (µ e, Σ e) for an expertise level e, we need to consider all the votes associated with e and derive the posterior distribution. We report the derived formula in the following, one can refer to [9; 23] for the detailed derivations. p(µ e, Σ e v iei =e, Θ) p({v i } ei =e µ e, Σ e) N G(µ k, Σ k µ 0, κ 0, α 0, β 0) = N (v µ e, Σ e) N G(µ k, Σ k µ 0, κ 0, α 0, β 0) v:{v i } ei =e = N G(µ e, Σ e µ e, κ e, α e, β e), (2) where µ e, κ e, α e, β e are defined as follows: µ e = κ0µ0 + ne v e. κ 0 + n e κ e = κ 0 + n e. α e = α 0 + ne 2. β e = β v:{v i } ei =e (v v e) 2 + κ0ne( ve µ0)2 κ 0 + n e. (3) where v e is the average vote score for expertise e, n e is the total number of votes with expertise level e. Given Eqn. 2 and Eqn. 3, we can update (µ e, Σ e) as follows: µ e = µ e. Σ e = α e. (4) β e With Gibbs Sampling, we can make the following parameter estimation: θ u,k = C k u + α K. user-topic distribution (5) k=1 Ck u + Kα C t k ψ k,t = + η T. t=1 Ct k + T η topic-tag distribution (6) C w k ϕ k,w = + γ V. w=1 Cw k + V γ topic-word distribution (7) C e k,u φ k,u,e = Σ E. e=1 Ce k,u + Eβ user topical expertise distribution (8) 4. CQARANK FOR TOPICAL EXPERTISE MEASURE TEM is a latent variable model for modeling textual contents and voting information to discover user topical interests and expertise. It does not make use of user network structure built from user Q&A graph. However, user network structure will be helpful for topical expertise learning because users who provide answers to high expertise level users tend to also be with a high expertise. Inspired by this intuition, we consider to extend PageRank to measure user topical expertise. The expertise of users under a specific topic in CQA can be interpreted as the authority of web pages in hyperlink environment. We propose CQARank to combine user topical interests and expertise learning results in TEM with link structure to enforce user topical expertise learning. CQARank could find experts not only with similar topical interests, but also with high topical expertise based on Q&A voting history in communities. First of all, we construct Q&A graph G = (V, E) in CQA. V is a set of vertex representing all users. E is a set of directed edges.

### Topical Authority Identification in Community Question Answering

Topical Authority Identification in Community Question Answering Guangyou Zhou, Kang Liu, and Jun Zhao National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences 95

### A Tri-Role Topic Model for Domain-Specific Question Answering

A Tri-Role Topic Model for Domain-Specific Question Answering Zongyang Ma Aixin Sun Quan Yuan Gao Cong School of Computer Engineering, Nanyang Technological University, Singapore 639798 {zma4, qyuan1}@e.ntu.edu.sg

### Subordinating to the Majority: Factoid Question Answering over CQA Sites

Journal of Computational Information Systems 9: 16 (2013) 6409 6416 Available at http://www.jofcis.com Subordinating to the Majority: Factoid Question Answering over CQA Sites Xin LIAN, Xiaojie YUAN, Haiwei

### Incorporating Participant Reputation in Community-driven Question Answering Systems

Incorporating Participant Reputation in Community-driven Question Answering Systems Liangjie Hong, Zaihan Yang and Brian D. Davison Department of Computer Science and Engineering Lehigh University, Bethlehem,

### Joint Relevance and Answer Quality Learning for Question Routing in Community QA

Joint Relevance and Answer Quality Learning for Question Routing in Community QA Guangyou Zhou, Kang Liu, and Jun Zhao National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy

Routing Questions for Collaborative Answering in Community Question Answering Shuo Chang Dept. of Computer Science University of Minnesota Email: schang@cs.umn.edu Aditya Pal IBM Research Email: apal@us.ibm.com

### Topic and Trend Detection in Text Collections using Latent Dirichlet Allocation

Topic and Trend Detection in Text Collections using Latent Dirichlet Allocation Levent Bolelli 1, Şeyda Ertekin 2, and C. Lee Giles 3 1 Google Inc., 76 9 th Ave., 4 th floor, New York, NY 10011, USA 2

### Question Routing by Modeling User Expertise and Activity in cqa services

Question Routing by Modeling User Expertise and Activity in cqa services Liang-Cheng Lai and Hung-Yu Kao Department of Computer Science and Information Engineering National Cheng Kung University, Tainan,

### MALLET-Privacy Preserving Influencer Mining in Social Media Networks via Hypergraph

MALLET-Privacy Preserving Influencer Mining in Social Media Networks via Hypergraph Janani K 1, Narmatha S 2 Assistant Professor, Department of Computer Science and Engineering, Sri Shakthi Institute of

### Network Big Data: Facing and Tackling the Complexities Xiaolong Jin

Network Big Data: Facing and Tackling the Complexities Xiaolong Jin CAS Key Laboratory of Network Data Science & Technology Institute of Computing Technology Chinese Academy of Sciences (CAS) 2015-08-10

### Learning to Recognize Reliable Users and Content in Social Media with Coupled Mutual Reinforcement

Learning to Recognize Reliable Users and Content in Social Media with Coupled Mutual Reinforcement Jiang Bian College of Computing Georgia Institute of Technology jbian3@mail.gatech.edu Eugene Agichtein

### Evolution of Experts in Question Answering Communities

Evolution of Experts in Question Answering Communities Aditya Pal, Shuo Chang and Joseph A. Konstan Department of Computer Science University of Minnesota Minneapolis, MN 55455, USA {apal,schang,konstan}@cs.umn.edu

### QASM: a Q&A Social Media System Based on Social Semantics

QASM: a Q&A Social Media System Based on Social Semantics Zide Meng, Fabien Gandon, Catherine Faron-Zucker To cite this version: Zide Meng, Fabien Gandon, Catherine Faron-Zucker. QASM: a Q&A Social Media

### KEYWORD SEARCH OVER PROBABILISTIC RDF GRAPHS

ABSTRACT KEYWORD SEARCH OVER PROBABILISTIC RDF GRAPHS In many real applications, RDF (Resource Description Framework) has been widely used as a W3C standard to describe data in the Semantic Web. In practice,

### Finding Expert Users in Community Question Answering

Finding Expert Users in Community Question Answering Fatemeh Riahi Faculty of Computer Science Dalhousie University riahi@cs.dal.ca Zainab Zolaktaf Faculty of Computer Science Dalhousie University zolaktaf@cs.dal.ca

### WHAT DEVELOPERS ARE TALKING ABOUT?

WHAT DEVELOPERS ARE TALKING ABOUT? AN ANALYSIS OF STACK OVERFLOW DATA 1. Abstract We implemented a methodology to analyze the textual content of Stack Overflow discussions. We used latent Dirichlet allocation

### Dynamical Clustering of Personalized Web Search Results

Dynamical Clustering of Personalized Web Search Results Xuehua Shen CS Dept, UIUC xshen@cs.uiuc.edu Hong Cheng CS Dept, UIUC hcheng3@uiuc.edu Abstract Most current search engines present the user a ranked

### Pharos: Social Map-Based Recommendation for Content-Centric Social Websites

Pharos: Social Map-Based Recommendation for Content-Centric Social Websites Wentao Zheng Michelle Zhou Shiwan Zhao Quan Yuan Xiatian Zhang Changyan Chi IBM Research China IBM Research Almaden ABSTRACT

### Question Quality in Community Question Answering Forums: A Survey

Question Quality in Community Question Answering Forums: A Survey ABSTRACT Antoaneta Baltadzhieva Tilburg University P.O. Box 90153 Tilburg, Netherlands a baltadzhieva@yahoo.de Community Question Answering

### FINDING EXPERT USERS IN COMMUNITY QUESTION ANSWERING SERVICES USING TOPIC MODELS

FINDING EXPERT USERS IN COMMUNITY QUESTION ANSWERING SERVICES USING TOPIC MODELS by Fatemeh Riahi Submitted in partial fulfillment of the requirements for the degree of Master of Computer Science at Dalhousie

### REVIEW ON QUERY CLUSTERING ALGORITHMS FOR SEARCH ENGINE OPTIMIZATION

Volume 2, Issue 2, February 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: A REVIEW ON QUERY CLUSTERING

### Topic models for Sentiment analysis: A Literature Survey

Topic models for Sentiment analysis: A Literature Survey Nikhilkumar Jadhav 123050033 June 26, 2014 In this report, we present the work done so far in the field of sentiment analysis using topic models.

### Integrated Expert Recommendation Model for Online Communities

Integrated Expert Recommendation Model for Online Communities Abeer El-korany 1 Computer Science Department, Faculty of Computers & Information, Cairo University ABSTRACT Online communities have become

### Learning to Suggest Questions in Online Forums

Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence Learning to Suggest Questions in Online Forums Tom Chao Zhou 1, Chin-Yew Lin 2,IrwinKing 3, Michael R. Lyu 1, Young-In Song 2

### Improving Question Retrieval in Community Question Answering Using World Knowledge

Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Improving Question Retrieval in Community Question Answering Using World Knowledge Guangyou Zhou, Yang Liu, Fang

### Exploiting Bilingual Translation for Question Retrieval in Community-Based Question Answering

Exploiting Bilingual Translation for Question Retrieval in Community-Based Question Answering Guangyou Zhou, Kang Liu and Jun Zhao National Laboratory of Pattern Recognition Institute of Automation, Chinese

### An Exploratory Study on Software Microblogger Behaviors

2014 4th IEEE Workshop on Mining Unstructured Data An Exploratory Study on Software Microblogger Behaviors Abstract Microblogging services are growing rapidly in the recent years. Twitter, one of the most

### Practical Graph Mining with R. 5. Link Analysis

Practical Graph Mining with R 5. Link Analysis Outline Link Analysis Concepts Metrics for Analyzing Networks PageRank HITS Link Prediction 2 Link Analysis Concepts Link A relationship between two entities

### Analyzing the Dynamics of Research by Extracting Key Aspects of Scientific Papers

Analyzing the Dynamics of Research by Extracting Key Aspects of Scientific Papers Sonal Gupta Christopher Manning Natural Language Processing Group Department of Computer Science Stanford University Columbia

### arxiv: v1 [cs.ir] 20 Dec 2016

Classification and Learning-to-rank Approaches for Cross-Device Matching at CIKM Cup 2016 Nam Khanh Tran L3S Research Center - Leibniz Universität Hannover ntran@l3s.de arxiv:1612.07117v1 [cs.ir] 20 Dec

### Comparing IPL2 and Yahoo! Answers: A Case Study of Digital Reference and Community Based Question Answering

Comparing and : A Case Study of Digital Reference and Community Based Answering Dan Wu 1 and Daqing He 1 School of Information Management, Wuhan University School of Information Sciences, University of

### Probabilistic topic models for sentiment analysis on the Web

University of Exeter Department of Computer Science Probabilistic topic models for sentiment analysis on the Web Chenghua Lin September 2011 Submitted by Chenghua Lin, to the the University of Exeter as

### Early Detection of Potential Experts in Question Answering Communities

Early Detection of Potential Experts in Question Answering Communities Aditya Pal 1, Rosta Farzan 2, Joseph A. Konstan 1, and Robert Kraut 2 1 Dept. of Computer Science and Engineering, University of Minnesota

### Latent Dirichlet Markov Allocation for Sentiment Analysis

Latent Dirichlet Markov Allocation for Sentiment Analysis Ayoub Bagheri Isfahan University of Technology, Isfahan, Iran Intelligent Database, Data Mining and Bioinformatics Lab, Electrical and Computer

### COMMUNITY QUESTION ANSWERING (CQA) services, Improving Question Retrieval in Community Question Answering with Label Ranking

Improving Question Retrieval in Community Question Answering with Label Ranking Wei Wang, Baichuan Li Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin, N.T., Hong

### Recommender Systems Seminar Topic : Application Tung Do. 28. Januar 2014 TU Darmstadt Thanh Tung Do 1

Recommender Systems Seminar Topic : Application Tung Do 28. Januar 2014 TU Darmstadt Thanh Tung Do 1 Agenda Google news personalization : Scalable Online Collaborative Filtering Algorithm, System Components

### A survey on click modeling in web search

A survey on click modeling in web search Lianghao Li Hong Kong University of Science and Technology Outline 1 An overview of web search marketing 2 An overview of click modeling 3 A survey on click models

### Social Media Mining. Data Mining Essentials

Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers

### Finding Similar Questions in Large Question and Answer Archives

Finding Similar Questions in Large Question and Answer Archives Jiwoon Jeon, W. Bruce Croft and Joon Ho Lee Center for Intelligent Information Retrieval, Computer Science Department University of Massachusetts,

### PSG College of Technology, Coimbatore-641 004 Department of Computer & Information Sciences BSc (CT) G1 & G2 Sixth Semester PROJECT DETAILS.

PSG College of Technology, Coimbatore-641 004 Department of Computer & Information Sciences BSc (CT) G1 & G2 Sixth Semester PROJECT DETAILS Project Project Title Area of Abstract No Specialization 1. Software

IADIS International Journal on WWW/Internet Vol. 12, No. 1, pp. 52-64 ISSN: 1645-7641 USER INTENT PREDICTION FROM ACCESS LOG IN ONLINE SHOP Hidekazu Yanagimoto. Osaka Prefecture University. 1-1, Gakuen-cho,

### Automatic Mining of Internet Translation Reference Knowledge Based on Multiple Search Engines

, 22-24 October, 2014, San Francisco, USA Automatic Mining of Internet Translation Reference Knowledge Based on Multiple Search Engines Baosheng Yin, Wei Wang, Ruixue Lu, Yang Yang Abstract With the increasing

### It Is Not Just What We Say, But How We Say Them: LDA-based Behavior-Topic Model

It Is Not Just What We Say, But How We Say Them: LDA-based Behavior-Topic Model Minghui Qiu Feida Zhu Jing Jiang Abstract Textual information exchanged among users on online social network platforms provides

21st International Congress on Modelling and Simulation, Gold Coast, Australia, 29 Nov to 4 Dec 2015 www.mssanz.org.au/modsim2015 On the Feasibility of Answer Suggestion for Advice-seeking Community Questions

### Search Result Optimization using Annotators

Search Result Optimization using Annotators Vishal A. Kamble 1, Amit B. Chougule 2 1 Department of Computer Science and Engineering, D Y Patil College of engineering, Kolhapur, Maharashtra, India 2 Professor,

### Which universities lead and lag? Toward university rankings based on scholarly output

Which universities lead and lag? Toward university rankings based on scholarly output Daniel Ramage and Christopher D. Manning Computer Science Department Stanford University Stanford, California 94305

### Personalizing Image Search from the Photo Sharing Websites

Personalizing Image Search from the Photo Sharing Websites Swetha.P.C, Department of CSE, Atria IT, Bangalore swethapc.reddy@gmail.com Aishwarya.P Professor, Dept.of CSE, Atria IT, Bangalore aishwarya_p27@yahoo.co.in

### Part 1: Link Analysis & Page Rank

Chapter 8: Graph Data Part 1: Link Analysis & Page Rank Based on Leskovec, Rajaraman, Ullman 214: Mining of Massive Datasets 1 Exam on the 5th of February, 216, 14. to 16. If you wish to attend, please

### Big Data Analytics of Multi-Relationship Online Social Network Based on Multi-Subnet Composited Complex Network

, pp.273-284 http://dx.doi.org/10.14257/ijdta.2015.8.5.24 Big Data Analytics of Multi-Relationship Online Social Network Based on Multi-Subnet Composited Complex Network Gengxin Sun 1, Sheng Bin 2 and

### Discovering Social Media Experts by Integrating Social Networks and Contents

Proceedings of the Twenty-Third Australasian Database Conference (ADC 2012), Melbourne, Australia Discovering Social Media Experts by Integrating Social Networks and Contents Zhao Zhang Bin Zhao Weining

### A Statistical Text Mining Method for Patent Analysis

A Statistical Text Mining Method for Patent Analysis Department of Statistics Cheongju University, shjun@cju.ac.kr Abstract Most text data from diverse document databases are unsuitable for analytical

### Data Mining Yelp Data - Predicting rating stars from review text

Data Mining Yelp Data - Predicting rating stars from review text Rakesh Chada Stony Brook University rchada@cs.stonybrook.edu Chetan Naik Stony Brook University cnaik@cs.stonybrook.edu ABSTRACT The majority

### PULLING OUT OPINION TARGETS AND OPINION WORDS FROM REVIEWS BASED ON THE WORD ALIGNMENT MODEL AND USING TOPICAL WORD TRIGGER MODEL

Journal homepage: www.mjret.in ISSN:2348-6953 PULLING OUT OPINION TARGETS AND OPINION WORDS FROM REVIEWS BASED ON THE WORD ALIGNMENT MODEL AND USING TOPICAL WORD TRIGGER MODEL Utkarsha Vibhute, Prof. Soumitra

### GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES

GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES ADVANCED AND ROBUST MOBILE SEARCH ENGINE FOR PERSONALIZED Mr. Yogesh B.Jadhao *1 and Assoc.Prof. Shyam S. Gupta 2 *1 Research scholar,computer Engineering,

### Hotel Review Search. CS410 Class Project. Abstract: Introduction: Shruthi Sudhanva (sudhanv2) Sinduja Subramaniam (ssbrmnm2) Guided by Hongning Wang

Hotel Review Search CS410 Class Project Shruthi Sudhanva (sudhanv2) Sinduja Subramaniam (ssbrmnm2) Guided by Hongning Wang Abstract: Hotel reviews are available in abundance today. This project focuses

### Link-based Analysis on Large Graphs. Presented by Weiren Yu Mar 01, 2011

Link-based Analysis on Large Graphs Presented by Weiren Yu Mar 01, 2011 Overview 1 Introduction 2 Problem Definition 3 Optimization Techniques 4 Experimental Results 2 1. Introduction Many applications

### Clustering Technique in Data Mining for Text Documents

Clustering Technique in Data Mining for Text Documents Ms.J.Sathya Priya Assistant Professor Dept Of Information Technology. Velammal Engineering College. Chennai. Ms.S.Priyadharshini Assistant Professor

### SUIT: A Supervised User-Item Based Topic Model for Sentiment Analysis

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence SUIT: A Supervised User-Item Based Topic Model for Sentiment Analysis Fangtao Li 1, Sheng Wang 2, Shenghua Liu 3 and Ming Zhang

### Expert Finding for Question Answering via Graph Regularized Matrix Completion

1 Expert Finding for Question Answering via Graph Regularized Matrix Completion Zhou Zhao, Lijun Zhang, Xiaofei He and Wilfred Ng Abstract Expert finding for question answering is a challenging problem

Volume 4, Issue 6, June 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Improving Web Image

### International Journal of Engineering Research-Online A Peer Reviewed International Journal Articles are freely available online:http://www.ijoer.

RESEARCH ARTICLE SURVEY ON PAGERANK ALGORITHMS USING WEB-LINK STRUCTURE SOWMYA.M 1, V.S.SREELAXMI 2, MUNESHWARA M.S 3, ANIL G.N 4 Department of CSE, BMS Institute of Technology, Avalahalli, Yelahanka,

### PROBABILISTIC MODELING IN COMMUNITY-BASED QUESTION ANSWERING SERVICES

PROBABILISTIC MODELING IN COMMUNITY-BASED QUESTION ANSWERING SERVICES by Zeinab Zolaktaf Zadeh Submitted in partial fulfillment of the requirements for the degree of Master of Computer Science at Dalhousie

### Distance Metric Learning in Data Mining (Part I) Fei Wang and Jimeng Sun IBM TJ Watson Research Center

Distance Metric Learning in Data Mining (Part I) Fei Wang and Jimeng Sun IBM TJ Watson Research Center 1 Outline Part I - Applications Motivation and Introduction Patient similarity application Part II

### Graph Processing and Social Networks

Graph Processing and Social Networks Presented by Shu Jiayu, Yang Ji Department of Computer Science and Engineering The Hong Kong University of Science and Technology 2015/4/20 1 Outline Background Graph

Ranking Community Answers by Modeling Question-Answer Relationships via Analogical Reasoning Xin-Jing Wang Microsoft Research Asia 4F Sigma, 49 Zhichun Road Beijing, P.R.China xjwang@microsoft.com Xudong

### 1 o Semestre 2007/2008

Departamento de Engenharia Informática Instituto Superior Técnico 1 o Semestre 2007/2008 Outline 1 2 3 4 5 Outline 1 2 3 4 5 Exploiting Text How is text exploited? Two main directions Extraction Extraction

### Micro blogs Oriented Word Segmentation System

Micro blogs Oriented Word Segmentation System Yijia Liu, Meishan Zhang, Wanxiang Che, Ting Liu, Yihe Deng Research Center for Social Computing and Information Retrieval Harbin Institute of Technology,

### The Application Research of Ant Colony Algorithm in Search Engine Jian Lan Liu1, a, Li Zhu2,b

3rd International Conference on Materials Engineering, Manufacturing Technology and Control (ICMEMTC 2016) The Application Research of Ant Colony Algorithm in Search Engine Jian Lan Liu1, a, Li Zhu2,b

### A Platform for Supporting Data Analytics on Twitter: Challenges and Objectives 1

A Platform for Supporting Data Analytics on Twitter: Challenges and Objectives 1 Yannis Stavrakas Vassilis Plachouras IMIS / RC ATHENA Athens, Greece {yannis, vplachouras}@imis.athena-innovation.gr Abstract.

### Ranking on Data Manifolds

Ranking on Data Manifolds Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, and Bernhard Schölkopf Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany {firstname.secondname

### Data Mining on Social Networks. Dionysios Sotiropoulos Ph.D.

Data Mining on Social Networks Dionysios Sotiropoulos Ph.D. 1 Contents What are Social Media? Mathematical Representation of Social Networks Fundamental Data Mining Concepts Data Mining Tasks on Digital

### Legal Informatics Final Paper Submission Creating a Legal-Focused Search Engine I. BACKGROUND II. PROBLEM AND SOLUTION

Brian Lao - bjlao Karthik Jagadeesh - kjag Legal Informatics Final Paper Submission Creating a Legal-Focused Search Engine I. BACKGROUND There is a large need for improved access to legal help. For example,

### A Classification-based Approach to Question Answering in Discussion Boards

A Classification-based Approach to Question Answering in Discussion Boards Liangjie Hong and Brian D. Davison Department of Computer Science and Engineering Lehigh University Bethlehem, PA 18015 USA {lih307,davison}@cse.lehigh.edu

### Social Media Mining. Network Measures

Klout Measures and Metrics 22 Why Do We Need Measures? Who are the central figures (influential individuals) in the network? What interaction patterns are common in friends? Who are the like-minded users

### A Comparative Study on Sentiment Classification and Ranking on Product Reviews

A Comparative Study on Sentiment Classification and Ranking on Product Reviews C.EMELDA Research Scholar, PG and Research Department of Computer Science, Nehru Memorial College, Putthanampatti, Bharathidasan

### 2009-04-15. The Representation and Storage of Combinatorial Block Designs Outline. Combinatorial Block Designs. Project Intro. External Representation

Combinatorial Block Designs 2009-04-15 Outline Project Intro External Representation Design Database System Deployment System Overview Conclusions 1. Since the project is a specific application in Combinatorial

### AMiner-mini: A People Search Engine For University

AMiner-mini: A People Search Engine For University Jingyuan Liu*, Debing Liu*, Xingyu Yan*, Li Dong #, Ting Zeng #, Yutao Zhang*, and Jie Tang* *Dept. of Com. Sci. and Tech., Tsinghua University # Tsinghua

### IT services for analyses of various data samples

IT services for analyses of various data samples Ján Paralič, František Babič, Martin Sarnovský, Peter Butka, Cecília Havrilová, Miroslava Muchová, Michal Puheim, Martin Mikula, Gabriel Tutoky Technical

### Effective Mentor Suggestion System for Collaborative Learning

Effective Mentor Suggestion System for Collaborative Learning Advait Raut 1 U pasana G 2 Ramakrishna Bairi 3 Ganesh Ramakrishnan 2 (1) IBM, Bangalore, India, 560045 (2) IITB, Mumbai, India, 400076 (3)

### Exploiting Topic based Twitter Sentiment for Stock Prediction

Exploiting Topic based Twitter Sentiment for Stock Prediction Jianfeng Si * Arjun Mukherjee Bing Liu Qing Li * Huayi Li Xiaotie Deng * Department of Computer Science, City University of Hong Kong, Hong

### Knowledge Sharing via Social Login: Exploiting Microblogging Service for Warming up Social Question Answering Websites

Knowledge Sharing via Social Login: Exploiting Microblogging Service for Warming up Social Question Answering Websites Yang Xiao 1, Wayne Xin Zhao 2, Kun Wang 1 and Zhen Xiao 1 1 School of Electronics

### Entropy based Graph Clustering: Application to Biological and Social Networks

Entropy based Graph Clustering: Application to Biological and Social Networks Edward C Kenley Young-Rae Cho Department of Computer Science Baylor University Complex Systems Definition Dynamically evolving

### Emoticon Smoothed Language Models for Twitter Sentiment Analysis

Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence Emoticon Smoothed Language Models for Twitter Sentiment Analysis Kun-Lin Liu, Wu-Jun Li, Minyi Guo Shanghai Key Laboratory of

### Incorporate Credibility into Context for the Best Social Media Answers

PACLIC 24 Proceedings 535 Incorporate Credibility into Context for the Best Social Media Answers Qi Su a,b, Helen Kai-yun Chen a, and Chu-Ren Huang a a Department of Chinese & Bilingual Studies, The Hong

### Continuous Fastest Path Planning in Road Networks by Mining Real-Time Traffic Event Information

Continuous Fastest Path Planning in Road Networks by Mining Real-Time Traffic Event Information Eric Hsueh-Chan Lu Chi-Wei Huang Vincent S. Tseng Institute of Computer Science and Information Engineering

### Searching Questions by Identifying Question Topic and Question Focus

Searching Questions by Identifying Question Topic and Question Focus Huizhong Duan 1, Yunbo Cao 1,2, Chin-Yew Lin 2 and Yong Yu 1 1 Shanghai Jiao Tong University, Shanghai, China, 200240 {summer, yyu}@apex.sjtu.edu.cn

### Design call center management system of e-commerce based on BP neural network and multifractal

Available online www.jocpr.com Journal of Chemical and Pharmaceutical Research, 2014, 6(6):951-956 Research Article ISSN : 0975-7384 CODEN(USA) : JCPRC5 Design call center management system of e-commerce

### Web based English-Chinese OOV term translation using Adaptive rules and Recursive feature selection

Web based English-Chinese OOV term translation using Adaptive rules and Recursive feature selection Jian Qu, Nguyen Le Minh, Akira Shimazu School of Information Science, JAIST Ishikawa, Japan 923-1292

### Mining Navigation Histories for User Need Recognition

Mining Navigation Histories for User Need Recognition Fabio Gasparetti and Alessandro Micarelli and Giuseppe Sansonetti Roma Tre University, Via della Vasca Navale 79, Rome, 00146 Italy {gaspare,micarel,gsansone}@dia.uniroma3.it

### Toward a community enhanced programming education

Toward a community enhanced programming education Ryo Suzuki University of Tokyo Tokyo, Japan 1253852881@mail.ecc.utokyo.ac.jp Permission to make digital or hard copies of all or part of this work for

### A Survey on Product Aspect Ranking

A Survey on Product Aspect Ranking Charushila Patil 1, Prof. P. M. Chawan 2, Priyamvada Chauhan 3, Sonali Wankhede 4 M. Tech Student, Department of Computer Engineering and IT, VJTI College, Mumbai, Maharashtra,

### A STUDY REGARDING INTER DOMAIN LINKED DOCUMENTS SIMILARITY AND THEIR CONSEQUENT BOUNCE RATE

STUDIA UNIV. BABEŞ BOLYAI, INFORMATICA, Volume LIX, Number 1, 2014 A STUDY REGARDING INTER DOMAIN LINKED DOCUMENTS SIMILARITY AND THEIR CONSEQUENT BOUNCE RATE DIANA HALIŢĂ AND DARIUS BUFNEA Abstract. Then

### Achieve Better Ranking Accuracy Using CloudRank Framework for Cloud Services

Achieve Better Ranking Accuracy Using CloudRank Framework for Cloud Services Ms. M. Subha #1, Mr. K. Saravanan *2 # Student, * Assistant Professor Department of Computer Science and Engineering Regional

### Identifying Focus, Techniques and Domain of Scientific Papers

Identifying Focus, Techniques and Domain of Scientific Papers Sonal Gupta Department of Computer Science Stanford University Stanford, CA 94305 sonal@cs.stanford.edu Christopher D. Manning Department of

### Online Courses Recommendation based on LDA

Online Courses Recommendation based on LDA Rel Guzman Apaza, Elizabeth Vera Cervantes, Laura Cruz Quispe, José Ochoa Luna National University of St. Agustin Arequipa - Perú {r.guzmanap,elizavvc,lvcruzq,eduardo.ol}@gmail.com

### RANKING WEB PAGES RELEVANT TO SEARCH KEYWORDS

ISBN: 978-972-8924-93-5 2009 IADIS RANKING WEB PAGES RELEVANT TO SEARCH KEYWORDS Ben Choi & Sumit Tyagi Computer Science, Louisiana Tech University, USA ABSTRACT In this paper we propose new methods for

### Towards SoMEST Combining Social Media Monitoring with Event Extraction and Timeline Analysis

Towards SoMEST Combining Social Media Monitoring with Event Extraction and Timeline Analysis Yue Dai, Ernest Arendarenko, Tuomo Kakkonen, Ding Liao School of Computing University of Eastern Finland {yvedai,