Sponsored Search Advertising. George Trimponias, CSE
|
|
- Todd Roberts
- 5 years ago
- Views:
Transcription
1 Sponsored Search Advertising form a Database Perspective George Trimponias, CSE 1
2 The 3 Stages of Sponsored Search Ad Selection: Select all candidate ads that may be relevant. Matchtype is important in this process. Ad Ranking: Rank candidate ads and select top-k. Industry Ranking Score: Max Bid Quality Score Ad Pricing: Determine the actual cost per click for every advertiser in the top-k list. Most prominent pricing scheme is the Generalized Second-Price Auction (GSP). 2
3 Account Structure Recall that advertiser information is hierarchically structured. Account. Ad Campaign. Related to a specific marketing goal. Characterized by a budget. Ad Group. Contains dozens of creatives and hundreds of bid terms. Maximum bid for each of the bid terms. 3
4 Ad Structure Headline. Lines of text. Display URL. Destination URL (Landing Page). 4
5 Database Example 5
6 Properties of Ad Corpora 6
7 Properties of Ad Corpora 7
8 Properties of Ad Corpora 8
9 Properties of Ad Corpora Ads can be indexed in main memory. Retrieving candidate ads for infrequent queries is a difficult problem. 9
10 Ad Indexing The retrieval of <creative, term> pairs from a structured schema. Structured retrieval problem, where the unit of retrieval is defined hierarchically. Naively indexing all possible retrieval units would result in wasted storage due to the Cartesian product semantics. To avoid this, we can utilize hierarchical indexing schemes that reduce the amount of duplication. 10
11 3 Main Approaches Term Coupling Index: Index units that are composed of <creative, term> pairs. Creative Coupling Index: The indexing unit is a single creative coupled with all the bid terms associated with its ad group. Ad Group Coupling Index: The indexing unit is the ad group itself. Different indexing strategies have a different impact on ad retrieval effectiveness. 11
12 Indexing for Broad Matchtype Broad Matchtype: a user s query contains all terms in the keyword in any order, possibly along with other terms. Consider the user query cheap used cars The bid phrase used cars matches the query. The bid phrase fast cars does not match the query. Inverse operation from classical document retrieval. In practice, broad match also accounts for singular or plural, synonyms and other variations, misspellings, extensions. 12
13 Traditional IR Techniques Fail Consider the use of inverted indexes containing ad IDs as postings. Using them, we obtain the union of the postings in the inverted indexes corresponding to keywords in the query. It is still necessary to filter out ads whose bid phrase contain words not in the query. This operation is not directly supported by inverted indexes! 13
14 A Simple Framework 1. Index the set of words in each ad phrase using a hash table. 2. Process queries by retrieving the entries associated with all subsets of words in a query. 14
15 Query Processing in the Simple Framework 1. Given a query Q, generate all subsets q. 2. Visit the corresponding nodes in the hash table. 3. Return all listings associated with ads for which the bid phrase is a subset of the query Q. Example with Q= new fiction books 15
16 Reducing Main Memory Latency in the Simple Framework Two Strategies 1. Traverse fewer data nodes. 2. Perform fewer hash-lookups against the hash table. 16
17 Traversing fewer data nodes Consider two ads A and B, for which bid_phrase(a) bid_phrase(b). We can remove the data of ad B to the data node corresponding to ad A. We call this data node remapping. 17
18 Traversing fewer data nodes Any query accessing the superset will by default have to access the subsets as well. We save one random access. We reduce the number of hash-entries by one. Sequential data reads versus random accesses. 18
19 Reducing the Number of Hash Lookups The number of subsets grows exponentially with the query length q. Solution: remap all long phrases to data nodes with node locators of length no more than k. k Number of hash lookups bounded by i = 1 as opposed to 2 q 1. q i 19
20 Optimizing the Index Structure The reduction in the number of data nodes leads to fewer random accesses, but come at the cost of the bigger amount of data we have to access per data node visited. We need to find the optimal tradeoff between these two factors. For this, we need the actual workload, i.e., the query history with the corresponding frequencies. The above problem is actually NP-complete (weighted set cover), but can be approximated within a reasonably good factor. 20
21 Relevance A very critical factor to the search engine s success. During the ad selection process, the search engine must identify low relevance ads and get rid of them. Different from the CTR, which is a measure of how attractive (as opposed to relevant) an ad is. 21
22 Prior Work on Relevance 1. Direct query-ad matching: ads are treated as documents and are ranked using a standard information retrieval technique. 2. Query Rewriting/Query Substitution: generate a relevant rewrite qj for a given query qi. 3. Query Recommendation/Query Clustering: consider the bipartite click graph of queries and ads with edges that correspond to click information. 22
23 Direct Query/Ad Matching Simple text overlap features. Important but insufficient. Consider the ad with title Best Jogging Shoes and a user searching for running gear. Historical click rates for a query-ad pair. When there is limited click history for a specific query-ad pair, back off to higher levels in the account hierarchy. Click Propensity in Query/Ad Translation. Translation-Based Systems. 23
24 Query Substitution Use query substitutions. A hybrid of exact and broad match. Has two phases: online and offline. Offline Phase: Fix a large set of sufficiently frequent queries Learn a function that substitutes input queries Online Phase: Use exact match to find ads matching the substitute query. 24
25 Substitution Framework 1. For each query, obtain the top S results returned by a Web Search Engine. 2. Find the k ads most related to the input query. 3. The bid phrases of the se ads form a pool of candidates. 4. The highest scoring bid phrase is selected as the query substitution. 25
26 Query Recommendation Consider the bipartite graph of queries and ads. An edge exists if and only if a user who issued the query clicked on the ad. The edge is also weighted with a positive weight, which represents the strength of the association. For instance, position-normalized CTR, or machinelearned estimate of the probability click P(click q,ad). 26
27 Query Recommendation through Collaborative Filtering 1. Compute the similarities between queries. 2. Compute a prediction of the response between a query and an ad based on how similar queries responded to the same ad. Reminiscent of PageRank 27
28 CTR CTR is the most prominent measure of ad quality employed by all large search engines. Crucial factor for ad ranking. Its estimation has attracted considerable attention in the scientific community. Its is usually formulated as a supervised learning problem. Maximum Entropy Model (EM). Nonlinear conjugate gradient descent algorithm. 28
29 Click Prediction as a Supervised Problem There is a set of training query-ad pairs (samples,) containing both click and non-click events. We want to estimate P(c q,a). We carefully select a proper set of features to represent the query-ad pair. Lexical Similarity Features Historical Performance of Ads 29
30 Personalized Click Prediction Estimate P(c q,a,u). We need to consider additional user features. Demographic Features (age, gender, marriage status, interests, job status, occupation) User-Specific Features Noisy Sparse 30
31 References Konig, Church, Markov. A Data Structure for Sponsored Search. ICDE, Bendersky et al. The Anatomy of an Ad: Structured Indexing and Retrieval for Sponsored Search. WWW, Hillard, D., Schroedl, S., Manavoglu, E., Raghavan, H., Leggetter, C. Improving Ad Relevance in sponsored Search. WSDM, Radlinski, F., Broder, A., Ciccolo, P., Gabrilovich, E., Josifovski, V., Riedel, L. Optimizing Relevance and Revenue in Ad Search: A Query Substitution Approach. SIGIR,
32 References Anastasakos, T., Hillard, D., Kshetramade, S., Raghavan, H. A Collaborative Filtering Approach to Ad ecommendation using the Query-Ad Click Graph. CIKM, Cheng, H., Cantu-Paz, E. Personalized Click Prediction in Sponsored Search. WSDM, Richardson, M., Dominowska, E., Ragno, R. Predicting Clicks: Estimating the Click-Through Rate for New Ads. WWW, Shen, S., Hu, B., Chen, W., Yang, Q. Personalized Click Model through Collaborative Filtering. WSDM,
The Sum of Its Parts: Reducing Sparsity in Click Estimation with Query Segments
The Sum of Its Parts: Reducing Sparsity in Click Estimation with Query Segments Dustin Hillard, Eren Manavoglu, Hema Raghavan, Chris Leggetter, Erick Cantú-Paz, and Rukmini Iyer Yahoo! Inc, 701 First Avenue,
More informationOnline Expansion of Rare Queries for Sponsored Search
Online Expansion of Rare Queries for Sponsored Search Peter Ciccolo, Evgeniy Gabrilovich, Vanja Josifovski, Don Metzler, Lance Riedel, Jeff Yuan Yahoo! Research 1 Sponsored Search 2 Sponsored Search in
More informationIntroduction p. 1 What is the World Wide Web? p. 1 A Brief History of the Web and the Internet p. 2 Web Data Mining p. 4 What is Data Mining? p.
Introduction p. 1 What is the World Wide Web? p. 1 A Brief History of the Web and the Internet p. 2 Web Data Mining p. 4 What is Data Mining? p. 6 What is Web Mining? p. 6 Summary of Chapters p. 8 How
More informationPart I: Data Mining Foundations
Table of Contents 1. Introduction 1 1.1. What is the World Wide Web? 1 1.2. A Brief History of the Web and the Internet 2 1.3. Web Data Mining 4 1.3.1. What is Data Mining? 6 1.3.2. What is Web Mining?
More informationPredictive Client-side Profiles for Keyword Advertising
Predictive Client-side Profiles for Keyword Advertising Mikhail Bilenko Microsoft Research Redmond, WA 98052, USA mbilenko@microsoft.com Matthew Richardson Microsoft Research Redmond, WA 98052, USA mattri@microsoft.com
More informationBing Liu. Web Data Mining. Exploring Hyperlinks, Contents, and Usage Data. With 177 Figures. Springer
Bing Liu Web Data Mining Exploring Hyperlinks, Contents, and Usage Data With 177 Figures Springer Table of Contents 1. Introduction 1 1.1. What is the World Wide Web? 1 1.2. A Brief History of the Web
More informationRecent advances in computational advertising: design and analysis of ad retrieval systems Evgeniy Gabrilovich
Recent advances in computational advertising: design and analysis of ad retrieval systems Evgeniy Gabrilovich gabr@yahoo-inc.com 1 What is Computational Advertising? A new scientific sub-discipline that
More informationOnline Expansion of Rare Queries for Sponsored Search
Online Expansion of Rare Queries for Sponsored Search Andrei Broder, Peter Ciccolo, Evgeniy Gabrilovich, Vanja Josifovski, Donald Metzler, Lance Riedel, Jeffrey Yuan Yahoo! Research 2821 Mission College
More informationQuery Refinement and Search Result Presentation
Query Refinement and Search Result Presentation (Short) Queries & Information Needs A query can be a poor representation of the information need Short queries are often used in search engines due to the
More informationwhite paper 4 Steps to Better Keyword Grouping Strategies for More Effective & Profitable Keyword Segmentation
white paper 4 Steps to Better Keyword Grouping Strategies for More Effective & Profitable Keyword Segmentation 2009, WordStream, Inc. All rights reserved. WordStream technologies are protected by pending
More informationChapter 27 Introduction to Information Retrieval and Web Search
Chapter 27 Introduction to Information Retrieval and Web Search Copyright 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 27 Outline Information Retrieval (IR) Concepts Retrieval
More informationEntity and Knowledge Base-oriented Information Retrieval
Entity and Knowledge Base-oriented Information Retrieval Presenter: Liuqing Li liuqing@vt.edu Digital Library Research Laboratory Virginia Polytechnic Institute and State University Blacksburg, VA 24061
More informationAnatomy of the Search Page
SEO Tips Anatomy of the Search Page Sitelinks Help users navigate site Sponsored Links Reach consumers at the moment they demonstrate interest Natural Search Universal Search Results generated Maps, Images,
More informationDavid March 22, 2017
. And How to Fix Them David Bird @Birdseyemktg March 22, 2017 What is AdWords 10 practices to keep your AdWords from going off the rails https://support.google.com/adwords 1 Courtesy Cary Pest.com Courtesy
More informationQuery Languages. Berlin Chen Reference: 1. Modern Information Retrieval, chapter 4
Query Languages Berlin Chen 2005 Reference: 1. Modern Information Retrieval, chapter 4 Data retrieval Pattern-based querying The Kinds of Queries Retrieve docs that contains (or exactly match) the objects
More informationScalable Multidimensional Hierarchical Bayesian Modeling on Spark
Scalable Multidimensional Hierarchical Bayesian Modeling on Spark Robert Ormandi, Hongxia Yang and Quan Lu Yahoo! Sunnyvale, CA 2015 Click-Through-Rate (CTR) Prediction Estimating the probability of click
More informationToday we show how a search engine works
How Search Engines Work Today we show how a search engine works What happens when a searcher enters keywords What was performed well in advance Also explain (briefly) how paid results are chosen If we
More informationInformation Retrieval Spring Web retrieval
Information Retrieval Spring 2016 Web retrieval The Web Large Changing fast Public - No control over editing or contents Spam and Advertisement How big is the Web? Practically infinite due to the dynamic
More informationPay-Per-Click Advertising Special Report
Pay-Per-Click Advertising Special Report Excerpted from 2005 by Kenneth A. McArthur All Rights Reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted by any means,
More informationCWS: : A Comparative Web Search System
CWS: : A Comparative Web Search System Jian-Tao Sun, Xuanhui Wang, Dou Shen Hua-Jun Zeng, Zheng Chen Microsoft Research Asia University of Illinois at Urbana-Champaign Hong Kong University of Science and
More informationDATA MINING - 1DL105, 1DL111
1 DATA MINING - 1DL105, 1DL111 Fall 2007 An introductory class in data mining http://user.it.uu.se/~udbl/dut-ht2007/ alt. http://www.it.uu.se/edu/course/homepage/infoutv/ht07 Kjell Orsborn Uppsala Database
More informationJan Pedersen 22 July 2010
Jan Pedersen 22 July 2010 Outline Problem Statement Best effort retrieval vs automated reformulation Query Evaluation Architecture Query Understanding Models Data Sources Standard IR Assumptions Queries
More informationAddressed Issue. P2P What are we looking at? What is Peer-to-Peer? What can databases do for P2P? What can databases do for P2P?
Peer-to-Peer Data Management - Part 1- Alex Coman acoman@cs.ualberta.ca Addressed Issue [1] Placement and retrieval of data [2] Server architectures for hybrid P2P [3] Improve search in pure P2P systems
More informationNew Technology Briefing
New Technology Briefing Kate Burns is Google s managing director of advertising sales UK, responsible for the growth of the company s UK market businesses and expansion. Kate was Google s first international
More informationInformation Retrieval
Information Retrieval CSC 375, Fall 2016 An information retrieval system will tend not to be used whenever it is more painful and troublesome for a customer to have information than for him not to have
More informationIntroduction to Computational Advertising. MS&E 239 Stanford University Autumn 2010 Instructors: Andrei Broder and Vanja Josifovski
Introduction to Computational Advertising MS&E 239 Stanford University Autumn 2010 Instructors: Andrei Broder and Vanja Josifovski 1 Lecture 5: Content match and IR ad selection 2 Disclaimers This talk
More informationAUDIT CHECKLIST. Webby Monks
Webby Monks AUDIT CHECKLIST STATUS KEYWORD ANALYSIS TIPS Keyword Conflicts A phrase added as a keyword as well as a negative keyword. Average Number of Keywords in an Ad Group 10-15 Keyword/Ad Group Search
More informationInformation Retrieval May 15. Web retrieval
Information Retrieval May 15 Web retrieval What s so special about the Web? The Web Large Changing fast Public - No control over editing or contents Spam and Advertisement How big is the Web? Practically
More informationmodern database systems lecture 4 : information retrieval
modern database systems lecture 4 : information retrieval Aristides Gionis Michael Mathioudakis spring 2016 in perspective structured data relational data RDBMS MySQL semi-structured data data-graph representation
More informationDATA MINING II - 1DL460. Spring 2014"
DATA MINING II - 1DL460 Spring 2014" A second course in data mining http://www.it.uu.se/edu/course/homepage/infoutv2/vt14 Kjell Orsborn Uppsala Database Laboratory Department of Information Technology,
More informationAdvanced Topics in Information Retrieval. Learning to Rank. ATIR July 14, 2016
Advanced Topics in Information Retrieval Learning to Rank Vinay Setty vsetty@mpi-inf.mpg.de Jannik Strötgen jannik.stroetgen@mpi-inf.mpg.de ATIR July 14, 2016 Before we start oral exams July 28, the full
More informationWebReach Product Glossary
WebReach Product Glossary September 2009 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z A Active Month Any month in which an account is being actively managed by hibu. Statuses that qualify as active
More informationDeep Character-Level Click-Through Rate Prediction for Sponsored Search
Deep Character-Level Click-Through Rate Prediction for Sponsored Search Bora Edizel - Phd Student UPF Amin Mantrach - Criteo Research Xiao Bai - Oath This work was done at Yahoo and will be presented as
More informationData-Driven Text Features for Sponsored Search Click Prediction
Data-Driven Text Features for Sponsored Search Click Prediction Benyah Shaparenko Cornell University 4130 Upson Hall Ithaca NY 14853 benyah@cs.cornell.edu Özgür Çetin Yahoo! Labs 111 West 40th New York
More informationLecture 5: Sponsored Search. ISM293 University of California, Santa Cruz Spring 2009 Instructors: Ram Akella, Andrei Broder, and Vanja Josifovski
Lecture 5: Sponsored Search ISM293 University of California, Santa Cruz Spring 2009 Instructors: Ram Akella, Andrei Broder, and Vanja Josifovski 1 Questions about last lecture? We welcome questions & suggestions
More informationKDD 10 Tutorial: Recommender Problems for Web Applications. Deepak Agarwal and Bee-Chung Chen Yahoo! Research
KDD 10 Tutorial: Recommender Problems for Web Applications Deepak Agarwal and Bee-Chung Chen Yahoo! Research Agenda Focus: Recommender problems for dynamic, time-sensitive applications Content Optimization
More informationDynamic Search Ads. Playbook
Dynamic Search s Playbook 1 WHAT Introduction to Dynamic Search s What are Dynamic Search s? Dynamic Search s offer a way to advertise on Google.com without keywords, allowing advertisers to harness their
More informationAdvanced Digital Markeitng Training Syllabus
Advanced Digital Markeitng Training Syllabus Digital Marketing Overview What is marketing? What is Digital Marketing? Understanding Marketing Process Why Digital Marketing Wins Over Traditional Marketing?
More informationScalable Semantic Matching of Queries to Ads in Sponsored Search. Yahoo Research, Advertising Sciences
Scalable Semantic Matching of Queries to Ads in Sponsored Search Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic, Fabrizio Silvestri, Ricardo Baeza- Yates, Andrew Feng, Erik Ordentlich, Lee Yang,
More informationCHAPTER THREE INFORMATION RETRIEVAL SYSTEM
CHAPTER THREE INFORMATION RETRIEVAL SYSTEM 3.1 INTRODUCTION Search engine is one of the most effective and prominent method to find information online. It has become an essential part of life for almost
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 informationESANN'2001 proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), April 2001, D-Facto public., ISBN ,
An Integrated Neural IR System. Victoria J. Hodge Dept. of Computer Science, University ofyork, UK vicky@cs.york.ac.uk Jim Austin Dept. of Computer Science, University ofyork, UK austin@cs.york.ac.uk Abstract.
More informationCSE 544 Principles of Database Management Systems
CSE 544 Principles of Database Management Systems Alvin Cheung Fall 2015 Lecture 5 - DBMS Architecture and Indexing 1 Announcements HW1 is due next Thursday How is it going? Projects: Proposals are due
More informationElementary IR: Scalable Boolean Text Search. (Compare with R & G )
Elementary IR: Scalable Boolean Text Search (Compare with R & G 27.1-3) Information Retrieval: History A research field traditionally separate from Databases Hans P. Luhn, IBM, 1959: Keyword in Context
More informationLearning Dense Models of Query Similarity from User Click Logs
Learning Dense Models of Query Similarity from User Click Logs Fabio De Bona, Stefan Riezler*, Keith Hall, Massi Ciaramita, Amac Herdagdelen, Maria Holmqvist Google Research, Zürich *Dept. of Computational
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 informationTutorial 1. Search engine success starts with keywords
Tutorial 1. Search engine success starts with keywords Welcome to the first of our seven lesson series on getting the most out of your first week with Wordtracker. We re going to show you how to amass
More informationChapter 6: Information Retrieval and Web Search. An introduction
Chapter 6: Information Retrieval and Web Search An introduction Introduction n Text mining refers to data mining using text documents as data. n Most text mining tasks use Information Retrieval (IR) methods
More informationSizzling Hot Tips for PPC & Local Search. Dave Kuhl Client Manager Lisa Sanner Snr. Client Manager Ben Krull Client Manager August 18, 2010
Sizzling Hot Tips for PPC & Local Search Dave Kuhl Client Manager Lisa Sanner Snr. Client Manager Ben Krull Client Manager August 18, 2010 Webinar Logistics & Introductions Being recorded Email with link
More informationWebsite Planning & Creation (14 hrs)
Website Planning & Creation (14 hrs) Understanding Internet Difference between Internet & web Understanding websites Understanding domain names & domain extensions What is web server & web hosting Different
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 informationPerformance Analysis of K-Mean Clustering on Normalized and Un-Normalized Information in Data Mining
Performance Analysis of K-Mean Clustering on Normalized and Un-Normalized Information in Data Mining Richa Rani 1, Mrs. Manju Bala 2 Student, CSE, JCDM College of Engineering, Sirsa, India 1 Asst Professor,
More informationVALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK VII SEMESTER
VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 603 203 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK VII SEMESTER CS6007-INFORMATION RETRIEVAL Regulation 2013 Academic Year 2018
More information60-538: Information Retrieval
60-538: Information Retrieval September 7, 2017 1 / 48 Outline 1 what is IR 2 3 2 / 48 Outline 1 what is IR 2 3 3 / 48 IR not long time ago 4 / 48 5 / 48 now IR is mostly about search engines there are
More informationInformation Retrieval. Chap 7. Text Operations
Information Retrieval Chap 7. Text Operations The Retrieval Process user need User Interface 4, 10 Text Text logical view Text Operations logical view 6, 7 user feedback Query Operations query Indexing
More informationCS224W: Social and Information Network Analysis Project Report: Edge Detection in Review Networks
CS224W: Social and Information Network Analysis Project Report: Edge Detection in Review Networks Archana Sulebele, Usha Prabhu, William Yang (Group 29) Keywords: Link Prediction, Review Networks, Adamic/Adar,
More informationCS473: Course Review CS-473. Luo Si Department of Computer Science Purdue University
CS473: CS-473 Course Review Luo Si Department of Computer Science Purdue University Basic Concepts of IR: Outline Basic Concepts of Information Retrieval: Task definition of Ad-hoc IR Terminologies and
More informationIn the Mood to Click? Towards Inferring Receptiveness to Search Advertising
In the Mood to Click? Towards Inferring Receptiveness to Search Advertising Qi Guo Eugene Agichtein Mathematics & Computer Science Department Emory University Atlanta, USA {qguo3,eugene}@mathcs.emory.edu
More informationCS377: Database Systems Text data and information. Li Xiong Department of Mathematics and Computer Science Emory University
CS377: Database Systems Text data and information retrieval Li Xiong Department of Mathematics and Computer Science Emory University Outline Information Retrieval (IR) Concepts Text Preprocessing Inverted
More informationEffective Latent Space Graph-based Re-ranking Model with Global Consistency
Effective Latent Space Graph-based Re-ranking Model with Global Consistency Feb. 12, 2009 1 Outline Introduction Related work Methodology Graph-based re-ranking model Learning a latent space graph A case
More informationRishiraj Saha Roy and Niloy Ganguly IIT Kharagpur India. Monojit Choudhury and Srivatsan Laxman Microsoft Research India India
Rishiraj Saha Roy and Niloy Ganguly IIT Kharagpur India Monojit Choudhury and Srivatsan Laxman Microsoft Research India India ACM SIGIR 2012, Portland August 15, 2012 Dividing a query into individual semantic
More informationOverview of DB & IR. ICS 624 Spring Asst. Prof. Lipyeow Lim Information & Computer Science Department University of Hawaii at Manoa
ICS 624 Spring 2011 Overview of DB & IR Asst. Prof. Lipyeow Lim Information & Computer Science Department University of Hawaii at Manoa 1/12/2011 Lipyeow Lim -- University of Hawaii at Manoa 1 Example
More informationDATA MINING II - 1DL460. Spring 2017
DATA MINING II - 1DL460 Spring 2017 A second course in data mining http://www.it.uu.se/edu/course/homepage/infoutv2/vt17 Kjell Orsborn Uppsala Database Laboratory Department of Information Technology,
More informationA Noise-aware Click Model for Web Search
A Noise-aware Click Model for Web Search Weizhu Chen Microsoft Research Asia wzchen@microsoft.com Zheng Chen Microsoft Research Asia zhengc@microsoft.com Dong Wang Microsoft Research Asia dongw89@gmail.com
More informationUniversity of Virginia Department of Computer Science. CS 4501: Information Retrieval Fall 2015
University of Virginia Department of Computer Science CS 4501: Information Retrieval Fall 2015 5:00pm-6:15pm, Monday, October 26th Name: ComputingID: This is a closed book and closed notes exam. No electronic
More informationShrey Patel B.E. Computer Engineering, Gujarat Technological University, Ahmedabad, Gujarat, India
International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2018 IJSRCSEIT Volume 3 Issue 3 ISSN : 2456-3307 Some Issues in Application of NLP to Intelligent
More informationCS246: Mining Massive Datasets Jure Leskovec, Stanford University
CS246: Mining Massive Datasets Jure Leskovec, Stanford University http://cs246.stanford.edu 2/24/2014 Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu 2 High dim. data
More informationCS6200 Information Retrieval. David Smith College of Computer and Information Science Northeastern University
CS6200 Information Retrieval David Smith College of Computer and Information Science Northeastern University Indexing Process!2 Indexes Storing document information for faster queries Indexes Index Compression
More informationNYU CSCI-GA Fall 2016
1 / 45 Information Retrieval: Personalization Fernando Diaz Microsoft Research NYC November 7, 2016 2 / 45 Outline Introduction to Personalization Topic-Specific PageRank News Personalization Deciding
More informationTHE URBAN COWGIRL PRESENTS KEYWORD RESEARCH
THE URBAN COWGIRL PRESENTS KEYWORD RESEARCH The most valuable keywords you have are the ones you mine from your pay-per-click performance reports. Scaling keywords that have proven to convert to orders
More informationCreating a Presence in Search Engine Results
Creating a Presence in Search Engine Results Melissa Rekos EVP, Digital Services Search Engine Marketing Paid placement (PPC: ranked by max CPC and relevance) Organic placement (SEO: ranked by relevance)
More informationOverture Advertiser Workbook. Chapter 4: Tracking Your Results
Overture Advertiser Workbook Chapter 4: Tracking Your Results Tracking Your Results TRACKING YOUR RESULTS Tracking the performance of your keywords enables you to effectively analyze your results, adjust
More informationInternational Journal of Scientific & Engineering Research Volume 2, Issue 12, December ISSN Web Search Engine
International Journal of Scientific & Engineering Research Volume 2, Issue 12, December-2011 1 Web Search Engine G.Hanumantha Rao*, G.NarenderΨ, B.Srinivasa Rao+, M.Srilatha* Abstract This paper explains
More informationWhere Next? Data Mining Techniques and Challenges for Trajectory Prediction. Slides credit: Layla Pournajaf
Where Next? Data Mining Techniques and Challenges for Trajectory Prediction Slides credit: Layla Pournajaf o Navigational services. o Traffic management. o Location-based advertising. Source: A. Monreale,
More informationA New Technique for Ranking Web Pages and Adwords
A New Technique for Ranking Web Pages and Adwords K. P. Shyam Sharath Jagannathan Maheswari Rajavel, Ph.D ABSTRACT Web mining is an active research area which mainly deals with the application on data
More informationSimple library thesaurus alignment with SILAS
Simple library thesaurus alignment with SILAS Roelant Ossewaarde 1 Linguistics Department University at Buffalo, the State University of New York rao3@buffalo.edu Abstract. This paper describes a system
More informationCandidate Document Retrieval for Arabic-based Text Reuse Detection on the Web
Candidate Document Retrieval for Arabic-based Text Reuse Detection on the Web Leena Lulu, Boumediene Belkhouche, Saad Harous College of Information Technology United Arab Emirates University Al Ain, United
More informationParallel learning of content recommendations using map- reduce
Parallel learning of content recommendations using map- reduce Michael Percy Stanford University Abstract In this paper, machine learning within the map- reduce paradigm for ranking
More information6 TOOLS FOR A COMPLETE MARKETING WORKFLOW
6 S FOR A COMPLETE MARKETING WORKFLOW 01 6 S FOR A COMPLETE MARKETING WORKFLOW FROM ALEXA DIFFICULTY DIFFICULTY MATRIX OVERLAP 6 S FOR A COMPLETE MARKETING WORKFLOW 02 INTRODUCTION Marketers use countless
More informationPrabhat Shah. Finding the right products to sell on Amazon and best practise selling techniques. Online Seller UK
Prabhat Shah Online Seller UK Finding the right products to sell on Amazon and best practise selling techniques @onlineselleruk https://www.slideshare.net/daytodayebay Background OSUK is a National ecommerce
More informationSklik PPC advertising from Seznam.cz
Sklik PPC advertising from Seznam.cz hotel šumava mujdummujhrad.cz mujdummujhrad.cz SUMMARY 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Seznam.cz overview Seznam.cz & figures About Sklik Sklik & figures Sklik in
More informationQuery Sugges*ons. Debapriyo Majumdar Information Retrieval Spring 2015 Indian Statistical Institute Kolkata
Query Sugges*ons Debapriyo Majumdar Information Retrieval Spring 2015 Indian Statistical Institute Kolkata Search engines User needs some information search engine tries to bridge this gap ssumption: the
More informationDo-It-Yourself Guide for Advertisers
Do-It-Yourself Guide for Advertisers Foreword Affinity's Advertiser DIY Guide is intended to provide Advertisers with helpful insights on how to best run their ad campaigns on the Affinity Ad Platform.
More informationKnowledge Retrieval. Franz J. Kurfess. Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A.
Knowledge Retrieval Franz J. Kurfess Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A. 1 Acknowledgements This lecture series has been sponsored by the European
More informationOutline. Morning program Preliminaries Semantic matching Learning to rank Entities
112 Outline Morning program Preliminaries Semantic matching Learning to rank Afternoon program Modeling user behavior Generating responses Recommender systems Industry insights Q&A 113 are polysemic Finding
More informationLogistics. CSE Case Studies. Indexing & Retrieval in Google. Review: AltaVista. BigTable. Index Stream Readers (ISRs) Advanced Search
CSE 454 - Case Studies Indexing & Retrieval in Google Some slides from http://www.cs.huji.ac.il/~sdbi/2000/google/index.htm Logistics For next class Read: How to implement PageRank Efficiently Projects
More informationInformation Retrieval CS Lecture 01. Razvan C. Bunescu School of Electrical Engineering and Computer Science
Information Retrieval CS 6900 Razvan C. Bunescu School of Electrical Engineering and Computer Science bunescu@ohio.edu Information Retrieval Information Retrieval (IR) is finding material of an unstructured
More informationSpatial and Temporal Aware, Trajectory Mobility Profile Based Location Management for Mobile Computing
Spatial and Temporal Aware, Trajectory Mobility Profile Based Location Management for Mobile Computing Jianting Zhang Le Gruenwald The University of Oklahoma, School of Computer Science, Norman, OK, 73019
More informationAnnouncement. Reading Material. Overview of Query Evaluation. Overview of Query Evaluation. Overview of Query Evaluation 9/26/17
Announcement CompSci 516 Database Systems Lecture 10 Query Evaluation and Join Algorithms Project proposal pdf due on sakai by 5 pm, tomorrow, Thursday 09/27 One per group by any member Instructor: Sudeepa
More informationExploring Reductions for Long Web Queries
Exploring Reductions for Long Web Queries Niranjan Balasubramanian University of Massachusetts Amherst 140 Governors Drive, Amherst, MA 01003 niranjan@cs.umass.edu Giridhar Kumaran and Vitor R. Carvalho
More informationINCREASED PPC & WEBSITE CONVERSIONS. AMMA MARKETING Online Marketing - Websites Big Agency Background & Small Agency Focus
INCREASED PPC & WEBSITE CONVERSIONS AMMA MARKETING Online Marketing - Websites Big Agency Background & Small Agency Focus ONLINE MARKETING AND WEBSITE DESIGN CASE STUDY Client: East Cost Service Cleaning
More informationIBE101: Introduction to Information Architecture. Hans Fredrik Nordhaug 2008
IBE101: Introduction to Information Architecture Hans Fredrik Nordhaug 2008 Objectives Defining IA Practicing IA User Needs and Behaviors The anatomy of IA Organizations Systems Labelling Systems Navigation
More informationQuery optimization. Elena Baralis, Silvia Chiusano Politecnico di Torino. DBMS Architecture D B M G. Database Management Systems. Pag.
Database Management Systems DBMS Architecture SQL INSTRUCTION OPTIMIZER MANAGEMENT OF ACCESS METHODS CONCURRENCY CONTROL BUFFER MANAGER RELIABILITY MANAGEMENT Index Files Data Files System Catalog DATABASE
More informationA Survey Of Different Text Mining Techniques Varsha C. Pande 1 and Dr. A.S. Khandelwal 2
A Survey Of Different Text Mining Techniques Varsha C. Pande 1 and Dr. A.S. Khandelwal 2 1 Department of Electronics & Comp. Sc, RTMNU, Nagpur, India 2 Department of Computer Science, Hislop College, Nagpur,
More informationResPubliQA 2010
SZTAKI @ ResPubliQA 2010 David Mark Nemeskey Computer and Automation Research Institute, Hungarian Academy of Sciences, Budapest, Hungary (SZTAKI) Abstract. This paper summarizes the results of our first
More informationThe Open University s repository of research publications and other research outputs. Search Personalization with Embeddings
Open Research Online The Open University s repository of research publications and other research outputs Search Personalization with Embeddings Conference Item How to cite: Vu, Thanh; Nguyen, Dat Quoc;
More informationThe Results of Falcon-AO in the OAEI 2006 Campaign
The Results of Falcon-AO in the OAEI 2006 Campaign Wei Hu, Gong Cheng, Dongdong Zheng, Xinyu Zhong, and Yuzhong Qu School of Computer Science and Engineering, Southeast University, Nanjing 210096, P. R.
More informationInformation Retrieval
Information Retrieval Learning to Rank Ilya Markov i.markov@uva.nl University of Amsterdam Ilya Markov i.markov@uva.nl Information Retrieval 1 Course overview Offline Data Acquisition Data Processing Data
More informationLecture 24: Image Retrieval: Part II. Visual Computing Systems CMU , Fall 2013
Lecture 24: Image Retrieval: Part II Visual Computing Systems Review: K-D tree Spatial partitioning hierarchy K = dimensionality of space (below: K = 2) 3 2 1 3 3 4 2 Counts of points in leaf nodes Nearest
More informationFeature Selecting Model in Automatic Text Categorization of Chinese Financial Industrial News
Selecting Model in Automatic Text Categorization of Chinese Industrial 1) HUEY-MING LEE 1 ), PIN-JEN CHEN 1 ), TSUNG-YEN LEE 2) Department of Information Management, Chinese Culture University 55, Hwa-Kung
More information