Competitive Prices as a Ranking System over Networks. Ehud Lehrer and Ady Pauzner Tel Aviv University July 2012
|
|
- Kelley Boone
- 5 years ago
- Views:
Transcription
1 Competitive Prices as a Ranking System over Networks Ehud Lehrer and Ady Pauzner Tel Aviv University July 2012
2 Ranking systems based (only) on network structure Examples: Science Citation Index: Rank of article = number of citations Google s PageRank: Link from a higher ranked item is worth more (Note circular definition)
3 Approaches to ranking Counting citations Citation index (Garfield 1960) Markov chain: PageRank (Wei 1951, Kendall 1955, Brin & Page 1998) Axiomatic: Palacios-Huerta and Volij (2004) Altman and Tennenholtz (2008): Axiomatization of PageRank Demange (2011): Separates quality and refereeing power Dynamics: Demange (2011): Ranking affects citations affect ranking Liebowitz and Palmer (1984): Iteration (impact adjusted) method
4 Our approach Construct economy based on the network of links Derive ranks from the competitive equilibrium prices
5 Pure exchange economy a reminder N consumer Each consumer brings an intial endowment (a basket of L goods) Each has a utility function In equilibrium each consumer sells his initial endowment and buys in exchange the best basket possible (subject to budget constraint) In equilibrium market clears
6 What is a network? N nodes (web page, article, friends) There are (directed) edges connecting between nodes Examples: web-page i gives a link to j; paper i gives a citation to paper j; i and j are friends (two edges)
7 Pure exchange economy in a network
8 Pure exchange economy in a network
9 Example 1
10 Cobb-Douglas utility
11 Example 2 (with Cobb-Douglas utility)
12 Example 2 (with Cobb-Douglas utility)
13 Quasi equilibrium (Debreu 1962) Definition: 1.Markets clear 2.Consumers with positive budget maximize utility subject to budget constraint 3.Consumers with 0 budget only required to satisfy budget constraint Differs from competitive equilibrium only for consumers with 0 budget who derive utility from a 0-priced goods They consume the leftovers, rather than an unbounded amount Quasi equilibrium exists under very mild conditions (utility functions continuous + sets of preferred baskets convex)
14 A little bit of notation
15 Cobb-Douglas general solution
16 Cobb-Douglas general solution
17 PageRank
18 PageRank
19 CES utility
20 Example 1 with CES utility In this example the parameter affects cardinal ranking but not ordinal (except for endpoints) We can easily generate examples where ordinal ranking changes
21 Economic vs. Markov approach The Markov approach can naturally generate only PageRank (equal transition probabilities) In order to replicated the CES equilibrium with the probabilistic approach, one would have to use transition probabilities that depend on the final invariant distribution weights That is, to get something different than PageRank, the model s definition would have to involve the outcome In the economic approach, the model is well defined with no circularity; the dependence of how agents split their budget on the prices of the goods comes from the solution concept (competitive equilibrium)
22 Uniqueness of Quasi Equilibrium
23 Example: multiple equilibria
24 The Citation Index
25 Rank and reviewing power Ranking system gives each article a quality score Each article also has a reviewing power (importance given by ranking system to its links) In PageRank, reviewing power rank In SCI/NCI, reviewing power independent of rank In world of internet, PageRank seems better In world of articles, PageRank is problematic: Case of articles on a timeline, that can only cite older articles. PageRank gives 0 to all of them, but the oldest PageRank works only with sufficient simultaneity
26 Rank and reviewing power
27 Economy with tax
28 Example 2 with tax
29 Tax can also change ordinal ranking
30 Ranking-biased agents
31 Ranking-biased agents
32 Ranking-biased agents
33 Ranking-biased agents
34 Ranking-biased agents
35 Ranking-biased agents
36 Ranking-biased agents
37 Summary Model of competitive economy as a device for ranking Ranking determined by choice of utility function Uniqueness of ranking holds at least for gross substitutes (and of course connected network) Cobb-Douglas economy yields PageRank No exchange economy yields SCI or NCI Minimum economy yields outcome of interaction between PageRank and linearly biased agents By adding a simple taxation scheme we can control how reviewing power depends on assessed quality
COMP 4601 Hubs and Authorities
COMP 4601 Hubs and Authorities 1 Motivation PageRank gives a way to compute the value of a page given its position and connectivity w.r.t. the rest of the Web. Is it the only algorithm: No! It s just one
More informationChapter 4: Utility Function
Econ Microeconomic Analysis Chapter : Utility Function Instructor: Hiroki Watanabe Spring Watanabe Econ Utility Function / Introduction Utility Function Indifference Curves Examples Trinity Now We Know
More informationAn Axiomatic Approach to Routing. Omer Lev, Moshe Tennenholtz and Aviv Zohar TARK 2015
An Axiomatic Approach to Routing Omer Lev, Moshe Tennenholtz and Aviv Zohar TARK 205 What is routing? A network of connected nodes with edge weights 5 3 2 3 4 What is routing? A network of connected nodes
More informationName. Final Exam, Economics 210A, December 2012 There are 8 questions. Answer as many as you can... Good luck!
Name Final Exam, Economics 210A, December 2012 There are 8 questions. Answer as many as you can... Good luck! 1) Let S and T be convex sets in Euclidean n space. Let S + T be the set {x x = s + t for some
More informationWeb search before Google. (Taken from Page et al. (1999), The PageRank Citation Ranking: Bringing Order to the Web.)
' Sta306b May 11, 2012 $ PageRank: 1 Web search before Google (Taken from Page et al. (1999), The PageRank Citation Ranking: Bringing Order to the Web.) & % Sta306b May 11, 2012 PageRank: 2 Web search
More informationCONSUMPTION BASICS. MICROECONOMICS Principles and Analysis Frank Cowell. July 2017 Frank Cowell: Consumption Basics 1
CONSUMPTION BASICS MICROECONOMICS Principles and Analysis Frank Cowell July 2017 Frank Cowell: Consumption Basics 1 Overview Consumption: Basics The setting The environment for the basic consumer optimisation
More informationThe principal behavioral postulate is. most preferred alternative from those
Chapter 5 Choice Economic Rationality The principal behavioral postulate is that a decisionmaker chooses its most preferred alternative from those available to it. The available choices constitute the
More informationCOMP Page Rank
COMP 4601 Page Rank 1 Motivation Remember, we were interested in giving back the most relevant documents to a user. Importance is measured by reference as well as content. Think of this like academic paper
More informationAlgorithms, Games, and Networks February 21, Lecture 12
Algorithms, Games, and Networks February, 03 Lecturer: Ariel Procaccia Lecture Scribe: Sercan Yıldız Overview In this lecture, we introduce the axiomatic approach to social choice theory. In particular,
More informationLecture 27: Learning from relational data
Lecture 27: Learning from relational data STATS 202: Data mining and analysis December 2, 2017 1 / 12 Announcements Kaggle deadline is this Thursday (Dec 7) at 4pm. If you haven t already, make a submission
More informationEC422 Mathematical Economics 2
EC422 Mathematical Economics 2 Chaiyuth Punyasavatsut Chaiyuth Punyasavatust 1 Course materials and evaluation Texts: Dixit, A.K ; Sydsaeter et al. Grading: 40,30,30. OK or not. Resources: ftp://econ.tu.ac.th/class/archan/c
More informationThe Fundamentals of Economic Dynamics and Policy Analyses: Learning through Numerical Examples. Part II. Dynamic General Equilibrium
The Fundamentals of Economic Dynamics and Policy Analyses: Learning through Numerical Examples. Part II. Dynamic General Equilibrium Hiroshi Futamura The objective of this paper is to provide an introductory
More informationWeb consists of web pages and hyperlinks between pages. A page receiving many links from other pages may be a hint of the authority of the page
Link Analysis Links Web consists of web pages and hyperlinks between pages A page receiving many links from other pages may be a hint of the authority of the page Links are also popular in some other information
More informationHow to Navigate Getting IPv4 Addresses in a Post Run-Out World. Amy Potter
How to Navigate Getting IPv4 Addresses in a Post Run-Out World Amy Potter Background Traditionally RIRs (and their predecessors) were the primary source of IPv4 addresses. Prior to the introduction of
More informationAn Agent-based Model for the Evolution of the Internet Ecosystem
An Agent-based Model for the Evolution of the Internet Ecosystem Amogh Dhamdhere Constantine Dovrolis Georgia Tech The Internet Ecosystem 27,000 autonomous networks independently operated and managed The
More informationMathematical Analysis of Google PageRank
INRIA Sophia Antipolis, France Ranking Answers to User Query Ranking Answers to User Query How a search engine should sort the retrieved answers? Possible solutions: (a) use the frequency of the searched
More informationIntroduction Optimization Geoff Gordon Ryan Tibshirani
Introduction 10-75 Optimization Geoff Gordon Ryan Tibshirani Administrivia http://www.cs.cmu.edu/~ggordon/1075-f1/ http://groups.google.com/group/1075-f1 Administrivia Prerequisites: no formal ones, but
More informationDSCI 575: Advanced Machine Learning. PageRank Winter 2018
DSCI 575: Advanced Machine Learning PageRank Winter 2018 http://ilpubs.stanford.edu:8090/422/1/1999-66.pdf Web Search before Google Unsupervised Graph-Based Ranking We want to rank importance based on
More informationMathematical Methods and Computational Algorithms for Complex Networks. Benard Abola
Mathematical Methods and Computational Algorithms for Complex Networks Benard Abola Division of Applied Mathematics, Mälardalen University Department of Mathematics, Makerere University Second Network
More informationPageRank and related algorithms
PageRank and related algorithms PageRank and HITS Jacob Kogan Department of Mathematics and Statistics University of Maryland, Baltimore County Baltimore, Maryland 21250 kogan@umbc.edu May 15, 2006 Basic
More informationROBERTO BATTITI, MAURO BRUNATO. The LION Way: Machine Learning plus Intelligent Optimization. LIONlab, University of Trento, Italy, Apr 2015
ROBERTO BATTITI, MAURO BRUNATO. The LION Way: Machine Learning plus Intelligent Optimization. LIONlab, University of Trento, Italy, Apr 2015 http://intelligentoptimization.org/lionbook Roberto Battiti
More informationPagerank Scoring. Imagine a browser doing a random walk on web pages:
Ranking Sec. 21.2 Pagerank Scoring Imagine a browser doing a random walk on web pages: Start at a random page At each step, go out of the current page along one of the links on that page, equiprobably
More informationCPSC 532L Project Development and Axiomatization of a Ranking System
CPSC 532L Project Development and Axiomatization of a Ranking System Catherine Gamroth cgamroth@cs.ubc.ca Hammad Ali hammada@cs.ubc.ca April 22, 2009 Abstract Ranking systems are central to many internet
More informationToru Kikuchi Kobe University. Abstract
Network externalities as a source of comparative advantage Toru Kikuchi Kobe University Abstract This note examines how the network externalities of communications activities and trading opportunities
More informationCPSC 340: Machine Learning and Data Mining. Ranking Fall 2016
CPSC 340: Machine Learning and Data Mining Ranking Fall 2016 Assignment 5: Admin 2 late days to hand in Wednesday, 3 for Friday. Assignment 6: Due Friday, 1 late day to hand in next Monday, etc. Final:
More informationCitation Services for Institutional Repositories: Citebase Search. Tim Brody Intelligence, Agents, Multimedia Group University of Southampton
Citation Services for Institutional Repositories: Citebase Search Tim Brody Intelligence, Agents, Multimedia Group University of Southampton 28/04/2009 2 28/04/2009 3 Content The Open Access Literature
More informationTextbook of Computable General Equilibrium Modelling
Textbook of Computable General Equilibrium Modelling Programming and Simulations Nobuhiro Hosoe Kenji Gasawa and Hideo Hashimoto Contents Abbreviations Symbols in CGE Models Tables, Figures and Lists Preface
More informationCitation Services for Institutional Repositories: Citebase Search. Tim Brody Intelligence, Agents, Multimedia Group University of Southampton
Citation Services for Institutional Repositories: Citebase Search Tim Brody Intelligence, Agents, Multimedia Group University of Southampton Content The Research Literature The Open Access Literature Why
More informationEc 181: Convex Analysis and Economic Theory
Division of the Humanities and Social Sciences Ec 181: Convex Analysis and Economic Theory KC Border Winter 2018 v. 2018.03.08::13.11 src: front KC Border: for Ec 181, Winter 2018 Woe to the author who
More informationCSE 258. Web Mining and Recommender Systems. Advanced Recommender Systems
CSE 258 Web Mining and Recommender Systems Advanced Recommender Systems This week Methodological papers Bayesian Personalized Ranking Factorizing Personalized Markov Chains Personalized Ranking Metric
More informationInformation Retrieval and Web Search Engines
Information Retrieval and Web Search Engines Lecture 7: Document Clustering May 25, 2011 Wolf-Tilo Balke and Joachim Selke Institut für Informationssysteme Technische Universität Braunschweig Homework
More informationReduce and Aggregate: Similarity Ranking in Multi-Categorical Bipartite Graphs
Reduce and Aggregate: Similarity Ranking in Multi-Categorical Bipartite Graphs Alessandro Epasto J. Feldman*, S. Lattanzi*, S. Leonardi, V. Mirrokni*. *Google Research Sapienza U. Rome Motivation Recommendation
More informationUnit VIII. Chapter 9. Link Analysis
Unit VIII Link Analysis: Page Ranking in web search engines, Efficient Computation of Page Rank using Map-Reduce and other approaches, Topic-Sensitive Page Rank, Link Spam, Hubs and Authorities (Text Book:2
More informationUsing Spam Farm to Boost PageRank p. 1/2
Using Spam Farm to Boost PageRank Ye Du Joint Work with: Yaoyun Shi and Xin Zhao University of Michigan, Ann Arbor Using Spam Farm to Boost PageRank p. 1/2 Roadmap Introduction: Link Spam and PageRank
More informationMarkov Decision Processes. (Slides from Mausam)
Markov Decision Processes (Slides from Mausam) Machine Learning Operations Research Graph Theory Control Theory Markov Decision Process Economics Robotics Artificial Intelligence Neuroscience /Psychology
More informationOptimizing Search Engines using Click-through Data
Optimizing Search Engines using Click-through Data By Sameep - 100050003 Rahee - 100050028 Anil - 100050082 1 Overview Web Search Engines : Creating a good information retrieval system Previous Approaches
More informationInformation Retrieval and Web Search
Information Retrieval and Web Search Link analysis Instructor: Rada Mihalcea (Note: This slide set was adapted from an IR course taught by Prof. Chris Manning at Stanford U.) The Web as a Directed Graph
More informationIntroduction to Information Retrieval
Introduction to Information Retrieval http://informationretrieval.org IIR 21: Link Analysis Hinrich Schütze Center for Information and Language Processing, University of Munich 2014-06-18 1/80 Overview
More informationInformation Retrieval. Lecture 11 - Link analysis
Information Retrieval Lecture 11 - Link analysis Seminar für Sprachwissenschaft International Studies in Computational Linguistics Wintersemester 2007 1/ 35 Introduction Link analysis: using hyperlinks
More informationprinceton univ. F 17 cos 521: Advanced Algorithm Design Lecture 24: Online Algorithms
princeton univ. F 17 cos 521: Advanced Algorithm Design Lecture 24: Online Algorithms Lecturer: Matt Weinberg Scribe:Matt Weinberg Lecture notes sourced from Avrim Blum s lecture notes here: http://www.cs.cmu.edu/
More informationONLINE EVALUATION FOR: Company Name
ONLINE EVALUATION FOR: Company Name Address Phone URL media advertising design P.O. Box 2430 Issaquah, WA 98027 (800) 597-1686 platypuslocal.com SUMMARY A Thank You From Platypus: Thank you for purchasing
More informationModel-based Recursive Partitioning
Model-based Recursive Partitioning Achim Zeileis Torsten Hothorn Kurt Hornik http://statmath.wu-wien.ac.at/ zeileis/ Overview Motivation The recursive partitioning algorithm Model fitting Testing for parameter
More informationUnsupervised Learning. Pantelis P. Analytis. Introduction. Finding structure in graphs. Clustering analysis. Dimensionality reduction.
March 19, 2018 1 / 40 1 2 3 4 2 / 40 What s unsupervised learning? Most of the data available on the internet do not have labels. How can we make sense of it? 3 / 40 4 / 40 5 / 40 Organizing the web First
More informationCS/INFO 1305 Summer 2009
Information Retrieval Information Retrieval (Search) IR Search Using a computer to find relevant pieces of information Text search Idea popularized in the article As We May Think by Vannevar Bush in 1945
More informationApplication of PageRank Algorithm on Sorting Problem Su weijun1, a
International Conference on Mechanics, Materials and Structural Engineering (ICMMSE ) Application of PageRank Algorithm on Sorting Problem Su weijun, a Department of mathematics, Gansu normal university
More informationConjoint analysis Outline
Outline Conjoint analysis as a decompositional preference model Steps in conjoint analysis Uses of conjoint analysis Compositional vs. decompositional preference models Compositional: respondents evaluate
More informationPart 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 Graph Data: Social Networks [Source: 4-degrees of separation, Backstrom-Boldi-Rosa-Ugander-Vigna,
More informationLecture #3: PageRank Algorithm The Mathematics of Google Search
Lecture #3: PageRank Algorithm The Mathematics of Google Search We live in a computer era. Internet is part of our everyday lives and information is only a click away. Just open your favorite search engine,
More informationIntroduction & Administrivia
Introduction & Administrivia Information Retrieval Evangelos Kanoulas ekanoulas@uva.nl Section 1: Unstructured data Sec. 8.1 2 Big Data Growth of global data volume data everywhere! Web data: observation,
More informationSocial Networks 2015 Lecture 10: The structure of the web and link analysis
04198250 Social Networks 2015 Lecture 10: The structure of the web and link analysis The structure of the web Information networks Nodes: pieces of information Links: different relations between information
More informationLearning to Rank Networked Entities
Learning to Rank Networked Entities Alekh Agarwal Soumen Chakrabarti Sunny Aggarwal Presented by Dong Wang 11/29/2006 We've all heard that a million monkeys banging on a million typewriters will eventually
More informationThe PageRank Citation Ranking
October 17, 2012 Main Idea - Page Rank web page is important if it points to by other important web pages. *Note the recursive definition IR - course web page, Brian home page, Emily home page, Steven
More informationA Modified Algorithm to Handle Dangling Pages using Hypothetical Node
A Modified Algorithm to Handle Dangling Pages using Hypothetical Node Shipra Srivastava Student Department of Computer Science & Engineering Thapar University, Patiala, 147001 (India) Rinkle Rani Aggrawal
More informationWeb Search. Lecture Objectives. Text Technologies for Data Science INFR Learn about: 11/14/2017. Instructor: Walid Magdy
Text Technologies for Data Science INFR11145 Web Search Instructor: Walid Magdy 14-Nov-2017 Lecture Objectives Learn about: Working with Massive data Link analysis (PageRank) Anchor text 2 1 The Web Document
More informationTODAY S LECTURE HYPERTEXT AND
LINK ANALYSIS TODAY S LECTURE HYPERTEXT AND LINKS We look beyond the content of documents We begin to look at the hyperlinks between them Address questions like Do the links represent a conferral of authority
More informationPAGE RANK ON MAP- REDUCE PARADIGM
PAGE RANK ON MAP- REDUCE PARADIGM Group 24 Nagaraju Y Thulasi Ram Naidu P Dhanush Chalasani Agenda Page Rank - introduction An example Page Rank in Map-reduce framework Dataset Description Work flow Modules.
More information10/10/13. Traditional database system. Information Retrieval. Information Retrieval. Information retrieval system? Information Retrieval Issues
COS 597A: Principles of Database and Information Systems Information Retrieval Traditional database system Large integrated collection of data Uniform access/modifcation mechanisms Model of data organization
More informationTo get the most from your website you need to consider some fundamentals.
Introduction To get the most from your website you need to consider some fundamentals. By using the correct techniques combined with patience, you will get better results than by trying to use short cuts
More informationInformation Retrieval
Introduction to Information Retrieval CS3245 12 Lecture 12: Crawling and Link Analysis Information Retrieval Last Time Chapter 11 1. Probabilistic Approach to Retrieval / Basic Probability Theory 2. Probability
More informationIntroduction to Dynamic Traffic Assignment
Introduction to Dynamic Traffic Assignment CE 392D January 22, 2018 WHAT IS EQUILIBRIUM? Transportation systems involve interactions among multiple agents. The basic facts are: Although travel choices
More informationPASSPORT USER GUIDE. This guide provides a detailed overview of how to use Passport, allowing you to find the information you need more efficiently.
PASSPORT USER GUIDE Passport is a global market research database providing insight on industries, economies and consumers worldwide, helping our clients analyse market context and identify future trends
More informationLecture 8: Linkage algorithms and web search
Lecture 8: Linkage algorithms and web search Information Retrieval Computer Science Tripos Part II Ronan Cummins 1 Natural Language and Information Processing (NLIP) Group ronan.cummins@cl.cam.ac.uk 2017
More informationWeb Structure, Age and Page Quality. Computer Science Department, University of Chile. Blanco Encalada 2120, Santiago, Chile.
Web Structure, Age and Page Quality Ricardo Baeza-Yates Felipe Saint-Jean Carlos Castillo Computer Science Department, University of Chile Blanco Encalada 2120, Santiago, Chile E-mail: frbaeza,fsaint,ccastillg@dcc.uchile.cl
More informationWhat is Retirement Reality?
What is Retirement Reality? How often do you reach out to your customers? Prospects? How much time do you spend trying to contact leads you ve purchased through a secondary service? Wouldn t it be great
More informationA Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data
An Efficient Privacy-Preserving Ranked Keyword Search Method Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop
More informationA brief history of Google
the math behind Sat 25 March 2006 A brief history of Google 1995-7 The Stanford days (aka Backrub(!?)) 1998 Yahoo! wouldn't buy (but they might invest...) 1999 Finally out of beta! Sergey Brin Larry Page
More informationMIDTERM EXAMINATION Networked Life (NETS 112) November 21, 2013 Prof. Michael Kearns
MIDTERM EXAMINATION Networked Life (NETS 112) November 21, 2013 Prof. Michael Kearns This is a closed-book exam. You should have no material on your desk other than the exam itself and a pencil or pen.
More informationPrinciples of Network Economics
Hagen Bobzin Principles of Network Economics SPIN Springer s internal project number, if known unknown Monograph August 12, 2005 Springer Berlin Heidelberg New York Hong Kong London Milan Paris Tokyo Contents
More informationThe Simplex Algorithm for LP, and an Open Problem
The Simplex Algorithm for LP, and an Open Problem Linear Programming: General Formulation Inputs: real-valued m x n matrix A, and vectors c in R n and b in R m Output: n-dimensional vector x There is one
More informationCS249: SPECIAL TOPICS MINING INFORMATION/SOCIAL NETWORKS
CS249: SPECIAL TOPICS MINING INFORMATION/SOCIAL NETWORKS Overview of Networks Instructor: Yizhou Sun yzsun@cs.ucla.edu January 10, 2017 Overview of Information Network Analysis Network Representation Network
More information( ) y #S, where! "[0,1]. In other words, if you connect any two
IX. ANALYSIS: FUNCTIONS, CORRESPONDENCES, AND FIXED POINTS Compact sets are nice because any function is guaranteed a maximum and a minimum on one. Today we start off by talking about another nice property
More informationLecture Notes to Big Data Management and Analytics Winter Term 2017/2018 Node Importance and Neighborhoods
Lecture Notes to Big Data Management and Analytics Winter Term 2017/2018 Node Importance and Neighborhoods Matthias Schubert, Matthias Renz, Felix Borutta, Evgeniy Faerman, Christian Frey, Klaus Arthur
More informationAn Agent-Based Adaptation of Friendship Games: Observations on Network Topologies
An Agent-Based Adaptation of Friendship Games: Observations on Network Topologies David S. Dixon University of New Mexico, Albuquerque NM 87131, USA Abstract. A friendship game in game theory is a network
More informationThe PageRank Citation Ranking: Bringing Order to the Web
The PageRank Citation Ranking: Bringing Order to the Web Marlon Dias msdias@dcc.ufmg.br Information Retrieval DCC/UFMG - 2017 Introduction Paper: The PageRank Citation Ranking: Bringing Order to the Web,
More informationCS 347 Parallel and Distributed Data Processing
CS 347 Parallel and Distributed Data Processing Spring 2016 Notes 12: Distributed Information Retrieval CS 347 Notes 12 2 CS 347 Notes 12 3 CS 347 Notes 12 4 CS 347 Notes 12 5 Web Search Engine Crawling
More informationCS 347 Parallel and Distributed Data Processing
CS 347 Parallel and Distributed Data Processing Spring 2016 Notes 12: Distributed Information Retrieval CS 347 Notes 12 2 CS 347 Notes 12 3 CS 347 Notes 12 4 Web Search Engine Crawling Indexing Computing
More informationRanking Functions. Linear-Constraint Loops
for Linear-Constraint Loops Amir Ben-Amram 1 for Loops Example 1 (GCD program): while (x > 1, y > 1) if x
More informationDISCRETE CONVEX ANALYSIS
DISCRETE CONVEX ANALYSIS o KAZUO MUROTA University of Tokyo; PRESTO, JST Tokyo, Japan Society for Industrial and Applied Mathematics Philadelphia List of Figures Notation Preface xi xiii xxi 1 Introduction
More informationEconomics: Principles in Action 2005 Correlated to: Indiana Family and Consumer Sciences Education, Consumer Economics (High School, Grades 9-12)
Indiana Family and Consumer Sciences Education, Consumer Economics Consumer Economics 1.0 PROCESSES: Explain, demonstrate, and integrate processes of thinking, communication, leadership, and management
More informationTargeting Nominal GDP or Prices: Expectation Dynamics and the Interest Rate Lower Bound
Targeting Nominal GDP or Prices: Expectation Dynamics and the Interest Rate Lower Bound Seppo Honkapohja, Bank of Finland Kaushik Mitra, University of Saint Andrews *Views expressed do not necessarily
More informationOPTIMIZATION, OPTIMAL DESIGN AND DE NOVO PROGRAMMING: DISCUSSION NOTES
OPTIMIZATION, OPTIMAL DESIGN AND DE NOVO PROGRAMMING: DISCUSSION NOTES MILAN ZELENY Introduction Fordham University, New York, USA mzeleny@fordham.edu Many older texts, with titles like Globally Optimal
More informationThe application of Randomized HITS algorithm in the fund trading network
The application of Randomized HITS algorithm in the fund trading network Xingyu Xu 1, Zhen Wang 1,Chunhe Tao 1,Haifeng He 1 1 The Third Research Institute of Ministry of Public Security,China Abstract.
More informationBrief (non-technical) history
Web Data Management Part 2 Advanced Topics in Database Management (INFSCI 2711) Textbooks: Database System Concepts - 2010 Introduction to Information Retrieval - 2008 Vladimir Zadorozhny, DINS, SCI, University
More informationInformation Retrieval Lecture 4: Web Search. Challenges of Web Search 2. Natural Language and Information Processing (NLIP) Group
Information Retrieval Lecture 4: Web Search Computer Science Tripos Part II Simone Teufel Natural Language and Information Processing (NLIP) Group sht25@cl.cam.ac.uk (Lecture Notes after Stephen Clark)
More informationMining Web Data. Lijun Zhang
Mining Web Data Lijun Zhang zlj@nju.edu.cn http://cs.nju.edu.cn/zlj Outline Introduction Web Crawling and Resource Discovery Search Engine Indexing and Query Processing Ranking Algorithms Recommender Systems
More informationCollaborative filtering based on a random walk model on a graph
Collaborative filtering based on a random walk model on a graph Marco Saerens, Francois Fouss, Alain Pirotte, Luh Yen, Pierre Dupont (UCL) Jean-Michel Renders (Xerox Research Europe) Some recent methods:
More informationGoogle Scholar Search Tips
Google Scholar Search Tips Google Scholar can be an extremely useful resource for your research, but it is not a substitute for searching your academic journal databases, such as Science Direct, JSTOR,
More informationPath Analysis References: Ch.10, Data Mining Techniques By M.Berry, andg.linoff Dr Ahmed Rafea
Path Analysis References: Ch.10, Data Mining Techniques By M.Berry, andg.linoff http://www9.org/w9cdrom/68/68.html Dr Ahmed Rafea Outline Introduction Link Analysis Path Analysis Using Markov Chains Applications
More information~ Ian Hunneybell: WWWT Revision Notes (15/06/2006) ~
. Search Engines, history and different types In the beginning there was Archie (990, indexed computer files) and Gopher (99, indexed plain text documents). Lycos (994) and AltaVista (995) were amongst
More informationWeb Structure Mining using Link Analysis Algorithms
Web Structure Mining using Link Analysis Algorithms Ronak Jain Aditya Chavan Sindhu Nair Assistant Professor Abstract- The World Wide Web is a huge repository of data which includes audio, text and video.
More informationChapter 2: Descriptive Statistics
Chapter 2: Descriptive Statistics Student Learning Outcomes By the end of this chapter, you should be able to: Display data graphically and interpret graphs: stemplots, histograms and boxplots. Recognize,
More informationSystem builder Cloud. User Guide. 1 st September 2017
System builder Cloud User Guide z 1 st September 2017 System Builder Cloud System builder is a Software tool to assist the sale of Navico Products as complete working systems TAKING THE FEAR OUT OF SELLING
More informationINTRODUCTION TO DATA SCIENCE. Link Analysis (MMDS5)
INTRODUCTION TO DATA SCIENCE Link Analysis (MMDS5) Introduction Motivation: accurate web search Spammers: want you to land on their pages Google s PageRank and variants TrustRank Hubs and Authorities (HITS)
More informationBibliometrics: Citation Analysis
Bibliometrics: Citation Analysis Many standard documents include bibliographies (or references), explicit citations to other previously published documents. Now, if you consider citations as links, academic
More informationarxiv: v1 [cs.ma] 8 May 2018
Ordinal Approximation for Social Choice, Matching, and Facility Location Problems given Candidate Positions Elliot Anshelevich and Wennan Zhu arxiv:1805.03103v1 [cs.ma] 8 May 2018 May 9, 2018 Abstract
More informationOutline. CS38 Introduction to Algorithms. Linear programming 5/21/2014. Linear programming. Lecture 15 May 20, 2014
5/2/24 Outline CS38 Introduction to Algorithms Lecture 5 May 2, 24 Linear programming simplex algorithm LP duality ellipsoid algorithm * slides from Kevin Wayne May 2, 24 CS38 Lecture 5 May 2, 24 CS38
More informationBig Data Analytics CSCI 4030
High dim. data Graph data Infinite data Machine learning Apps Locality sensitive hashing PageRank, SimRank Filtering data streams SVM Recommen der systems Clustering Community Detection Web advertising
More informationAgenda. Math Google PageRank algorithm. 2 Developing a formula for ranking web pages. 3 Interpretation. 4 Computing the score of each page
Agenda Math 104 1 Google PageRank algorithm 2 Developing a formula for ranking web pages 3 Interpretation 4 Computing the score of each page Google: background Mid nineties: many search engines often times
More informationPartially Observable Markov Decision Processes. Mausam (slides by Dieter Fox)
Partially Observable Markov Decision Processes Mausam (slides by Dieter Fox) Stochastic Planning: MDPs Static Environment Fully Observable Perfect What action next? Stochastic Instantaneous Percepts Actions
More informationCourse Summary Homework
Course Summary Homework (Max useful score: 100 - Available points: 210) 15-382: Collective Intelligence (Spring 2018) OUT: April 21, 2018, at 1:00am DUE: May 1, 2018 at 1pm - Available late days: 0 Instructions
More information