SlideShare a Scribd company logo
1 of 22
Submitted by
Somipam R. Shimray
Iii/semester dept. of LIS
Pondicherry University
INTRODUCTION
 Evaluation is a systematic determination of a subject's merit, worth
and significance, using criteria governed by a set of standards.
 It ascertain the degree of achievement in regard to the aim and
objectives and results of any such action that has been completed.
 Evaluation of information retrieval system measure which of the two
existing system perform better and try to assess how the level of
performance of a given can be improved.
 Lancaster state that evaluation of information retrieval system can be
justified by the following three issues:
 How well the system is satisfying its objectives
 How efficiently it is satisfying its objectives and
 Whether the system justified its existence.
PURPOSE OF EVALUATION
 The main purpose of the evaluation is to focus on the process of
implementation rather than on its impact.
 Evaluation studies also investigate the degree to which the state goals
have been achieved to which these can be achieved.
 To measure information retrieval effectiveness in the standard
way, we need a test collection consisting of three things:
 A document collection.
 A test suite of information needs, expressible as queries.
 A set of relevance judgments, standardly a binary assessment of
either relevant or non relevant for each query-document pair.
PURPOSE OF EVALUATION
Swanson state seven purposes for evaluation:
 To assess a set of goals, a programme plan, or a design prior to
implementation.
 To determine whether and how well goals or performance
expectation are being fulfilled.
 To determine specific reasons for success and failure.
 To uncover principles underlying a successful programme.
 To explore technique for increasing programme effectiveness.
 To established a foundation of further research on the reason for
the relative success of alternative technique and
 To improve the means employed for attaining objectives or to
redefine sub goals or goals in view of research findings.
PURPOSE OF EVALUATION
Keen give three major purpose of evaluation for an information
retrieval system:
 The need for measures with which to make merit comparisons
within a single test situation. In other words, evaluation studies
are conducted to compare the merits or demerits of two or more
system.
 The need for measure with which to make comparison between
results obtained in different test situation and
 The need for assessing the merit of a real-life system.
EVALUATION CRITERIA
 Evaluation of Information Retrieval can be conduct into two
different viewpoints.
 Managerial viewpoint: when evaluation is conducted from
managerial point of view it is called managerial oriented
evaluation.
 User viewpoint: when evaluation is conducted from the user point
of view it is called user-oriented evaluation study.
EVALUATION CRITERIA
Lancaster in 1971 proposed five Evaluation Criteria
 Coverage of the system
 Ability of the system to retrieve wanted items (i.e. recall).
 Ability of the system to avoid retrieval of unwanted items (i.e.
precision).
 The response time of the system and
 The amount of effort required by the user.
EVALUATION CRITERIA
 Recall
 Precision
 Fallout
 Generality
EVALUATION CRITERIA
Recall
 Recall is defined as the proportion of the total relevant documents
that is retrieved.
Number of relevant item retrieved
Recall=——————————————————————x 100
Total number of relevant items in the collection
 Example If there are 100 documents in a collection that are relevant
to a given query and 60 of these items are retrieved in a given
search, then the recall is state to be 60% in other words the system
has been able to retrieve 60% of the relevant items.
60
Recall= ———x 100
100
Recall= 60%
Total number of relevant items in the collection=100
Number of relevant item retrieved=60
Recall
Total no. of relevent
items
no. of relevent items
EVALUATION CRITERIA
Precision
 Precision is defined as the proportion of documents retrieved that is
relevant.
Number of relevant item retrieved
Precision=——————————————————x 100
Total number of items retrieved
 Example In a given search the system retrieves 80 items, out of
which 40 are relevant and 40 are non-relevant, the precision is 50%.
40
Precision= —————x 100
80
Precision= 50%
Total number of items retrieved=80
Number of relevant item retrieved=40
Precision
Total no. of relevent
items in the collection
Total no. of items
retrieved
No. of relevent items
retrieved
Recall-Precision matrix
R= [a/ (a+c)] x 100
P= [a/ (a+b)] x 100
Relevant Not-relevant Total
Retrieved a (hints) b (noise) a+b
Not-retrieved c (misses) d rejected c+d
Total a+c b+d a+b+c+d
EVALUATION CRITERIA
Fallout
 Fallout ratio is the proportion of non-relevant items that has been
retrieved in a given search.
Generality
 Generality ratio is the relevant items that have been retrieved in a
given search.
Retrieval Measure
Symbol Evaluation measure Formula Explanation
R Recall a/(a+c) Proportion of relevant
items retrieved
P Precision a/(a+b) Proportion of retrieved
item that are relevant
F Fallout b/(b+d) Proportion of non-relevant
items retrieved
G Generality (a+c)/(a+b+c+d) Proportion of relevant
items per query
LIMITATIONS OF RECALL AND
PRECISION
 Different users may want different levels of recall.
 A person going to prepare a state-of-the-art report on a topic would
like to have all the items available on the topics and therefore will go
for high recall.
 Where as a user wanting to know about a given topic will prefer to
have a few items and thus will not require a high recall.
OTHER EVALUATION CRITERIA
 Effectiveness
 Efficiency
 Usability
 Satisfaction
 Cost
OTHER EVALUATION CRITERIA
Effectiveness
 Effectiveness means the level up to which the given system attained
its objectives.
 In an information retrieval system effectiveness may be measure of
how far it can retrieve relevant information while with-holding non-
relevant information.
Efficiency
 Efficiency means how economically the system is achieving its
objectives.
 In an information retrieval system efficiency can be measured be
factor such as cost.
 Cost include factor such as response time, time taken by the system
to provide an answer.
OTHER EVALUATION CRITERIA
Usability
 Measure that embraces the interface through which the user interacts
with the system.
 Takes into account the user and their expectations, skills and
experiences.
Satisfaction
 Search task
 Search setting
 The searcher’s state contributes to quality and satisfaction judgments
in digital environments.
 Other perspectives on satisfaction are to be found in the service
quality and website quality literatures.
OTHER EVALUATION CRITERIA
Cost
 Users may experience costs in terms of any payment that they need
to make for system or document access.
 Most significant cost is associated with the time that they spend in
searching a system.
CONCLUSION
 Evaluation is a systematic determination of a subject's merit, worth
and significance, using criteria governed by a set of standards.
 Effectiveness and efficiency are two basic parameter for measuring
the performance of system.
 Evaluation criteria
 Managerial viewpoint: when evaluation is conducted from
managerial point of view it is called managerial oriented
evaluation.
 User viewpoint: when evaluation is conducted from the user point
of view it is called user-oriented evaluation study.
 Recall, Precision, Fallout and Generality.
Ppt evaluation of information retrieval system

More Related Content

What's hot

Information searching & retrieving techniques khalid
Information searching & retrieving techniques khalidInformation searching & retrieving techniques khalid
Information searching & retrieving techniques khalidKhalid Mahmood
 
Informatio retrival evaluation
Informatio retrival evaluationInformatio retrival evaluation
Informatio retrival evaluationNidhirBiswas
 
Electronic Resource Management in the library
Electronic Resource Management in the libraryElectronic Resource Management in the library
Electronic Resource Management in the libraryDr. Nihar K. Patra
 
Indexing language concept types and characteristics
Indexing language concept types and characteristicsIndexing language concept types and characteristics
Indexing language concept types and characteristicsDr. Utpal Das
 
Library consortia
Library consortia Library consortia
Library consortia Dheeraj Negi
 
Information retrieval s
Information retrieval sInformation retrieval s
Information retrieval ssilambu111
 
WEB BASED INFORMATION RETRIEVAL SYSTEM
WEB BASED INFORMATION RETRIEVAL SYSTEMWEB BASED INFORMATION RETRIEVAL SYSTEM
WEB BASED INFORMATION RETRIEVAL SYSTEMSai Kumar Ale
 
Library Automation sofrwere
Library Automation sofrwereLibrary Automation sofrwere
Library Automation sofrwereDeepak Malviya
 
Post coordinate indexing .. Library and information science
Post coordinate indexing .. Library and information sciencePost coordinate indexing .. Library and information science
Post coordinate indexing .. Library and information scienceharshaec
 
Probabilistic retrieval model
Probabilistic retrieval modelProbabilistic retrieval model
Probabilistic retrieval modelbaradhimarch81
 
INFORMATION RETRIEVAL Anandraj.L
INFORMATION RETRIEVAL Anandraj.LINFORMATION RETRIEVAL Anandraj.L
INFORMATION RETRIEVAL Anandraj.Lanujessy
 
Library automation software
Library automation softwareLibrary automation software
Library automation softwareJancypriya M
 
Chain indexing
Chain indexingChain indexing
Chain indexingsilambu111
 

What's hot (20)

Information searching & retrieving techniques khalid
Information searching & retrieving techniques khalidInformation searching & retrieving techniques khalid
Information searching & retrieving techniques khalid
 
Resources Sharing
Resources SharingResources Sharing
Resources Sharing
 
Informatio retrival evaluation
Informatio retrival evaluationInformatio retrival evaluation
Informatio retrival evaluation
 
Electronic Resource Management in the library
Electronic Resource Management in the libraryElectronic Resource Management in the library
Electronic Resource Management in the library
 
Information Retrieval
Information RetrievalInformation Retrieval
Information Retrieval
 
Indexing language concept types and characteristics
Indexing language concept types and characteristicsIndexing language concept types and characteristics
Indexing language concept types and characteristics
 
Library consortia
Library consortia Library consortia
Library consortia
 
Library Classification
Library ClassificationLibrary Classification
Library Classification
 
Information retrieval s
Information retrieval sInformation retrieval s
Information retrieval s
 
WEB BASED INFORMATION RETRIEVAL SYSTEM
WEB BASED INFORMATION RETRIEVAL SYSTEMWEB BASED INFORMATION RETRIEVAL SYSTEM
WEB BASED INFORMATION RETRIEVAL SYSTEM
 
Uniterm indexing
Uniterm indexing Uniterm indexing
Uniterm indexing
 
Library automation
Library automationLibrary automation
Library automation
 
Library Automation sofrwere
Library Automation sofrwereLibrary Automation sofrwere
Library Automation sofrwere
 
Post coordinate indexing .. Library and information science
Post coordinate indexing .. Library and information sciencePost coordinate indexing .. Library and information science
Post coordinate indexing .. Library and information science
 
Probabilistic retrieval model
Probabilistic retrieval modelProbabilistic retrieval model
Probabilistic retrieval model
 
INFORMATION RETRIEVAL Anandraj.L
INFORMATION RETRIEVAL Anandraj.LINFORMATION RETRIEVAL Anandraj.L
INFORMATION RETRIEVAL Anandraj.L
 
Library automation software
Library automation softwareLibrary automation software
Library automation software
 
CAS & SDI service
CAS & SDI serviceCAS & SDI service
CAS & SDI service
 
Chain indexing
Chain indexingChain indexing
Chain indexing
 
Oclc
OclcOclc
Oclc
 

Viewers also liked

Management information and evaluation system
Management information and evaluation systemManagement information and evaluation system
Management information and evaluation systemGagan Preet
 
Introduction to Information System
Introduction to Information SystemIntroduction to Information System
Introduction to Information SystemGiO Friginal
 
Technology Management, Case IT planning
Technology Management, Case IT planningTechnology Management, Case IT planning
Technology Management, Case IT planningYogesh Garg
 
use of IT in supply chain management
use of IT in supply chain managementuse of IT in supply chain management
use of IT in supply chain managementRohit Bhabal
 
Spiral model presentation
Spiral model presentationSpiral model presentation
Spiral model presentationSayedFarhan110
 
Role of IT in Supply Chain Management
Role of IT in Supply Chain ManagementRole of IT in Supply Chain Management
Role of IT in Supply Chain ManagementSindoor Naik
 
Role of information Technology in Supply Chain Manageent
Role of information Technology in Supply Chain ManageentRole of information Technology in Supply Chain Manageent
Role of information Technology in Supply Chain ManageentAnand Jha
 
Data warehouse architecture
Data warehouse architectureData warehouse architecture
Data warehouse architecturepcherukumalla
 
DATA WAREHOUSING
DATA WAREHOUSINGDATA WAREHOUSING
DATA WAREHOUSINGKing Julian
 
Logical design vs physical design
Logical design vs physical designLogical design vs physical design
Logical design vs physical designMd. Mahedi Mahfuj
 
Data mining slides
Data mining slidesData mining slides
Data mining slidessmj
 
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017Carol Smith
 

Viewers also liked (17)

Management information and evaluation system
Management information and evaluation systemManagement information and evaluation system
Management information and evaluation system
 
Introduction to Information System
Introduction to Information SystemIntroduction to Information System
Introduction to Information System
 
Technology Management, Case IT planning
Technology Management, Case IT planningTechnology Management, Case IT planning
Technology Management, Case IT planning
 
Prototype Model
Prototype ModelPrototype Model
Prototype Model
 
use of IT in supply chain management
use of IT in supply chain managementuse of IT in supply chain management
use of IT in supply chain management
 
Spiral model
Spiral modelSpiral model
Spiral model
 
Spiral model presentation
Spiral model presentationSpiral model presentation
Spiral model presentation
 
Role of IT in Supply Chain Management
Role of IT in Supply Chain ManagementRole of IT in Supply Chain Management
Role of IT in Supply Chain Management
 
Role of information Technology in Supply Chain Manageent
Role of information Technology in Supply Chain ManageentRole of information Technology in Supply Chain Manageent
Role of information Technology in Supply Chain Manageent
 
DATA WAREHOUSING AND DATA MINING
DATA WAREHOUSING AND DATA MININGDATA WAREHOUSING AND DATA MINING
DATA WAREHOUSING AND DATA MINING
 
Data warehouse architecture
Data warehouse architectureData warehouse architecture
Data warehouse architecture
 
End user development
End user developmentEnd user development
End user development
 
DATA WAREHOUSING
DATA WAREHOUSINGDATA WAREHOUSING
DATA WAREHOUSING
 
Logical design vs physical design
Logical design vs physical designLogical design vs physical design
Logical design vs physical design
 
Data mining slides
Data mining slidesData mining slides
Data mining slides
 
Data mining
Data miningData mining
Data mining
 
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017
 

Similar to Ppt evaluation of information retrieval system

Data collection and reporting of key performance indicators
Data collection and reporting of key performance indicatorsData collection and reporting of key performance indicators
Data collection and reporting of key performance indicatorskiran
 
Program evaluation
Program evaluationProgram evaluation
Program evaluationYen Bunsoy
 
Evaluation of an Information System.pptx
Evaluation of an Information System.pptxEvaluation of an Information System.pptx
Evaluation of an Information System.pptxDrIrfanulHaqAkhoon
 
Example of quality management system
Example of quality management systemExample of quality management system
Example of quality management systemselinasimpson1701
 
Health Informatics- Module 4-Chapter 1.pptx
Health Informatics- Module 4-Chapter 1.pptxHealth Informatics- Module 4-Chapter 1.pptx
Health Informatics- Module 4-Chapter 1.pptxArti Parab Academics
 
Quality Assurance Triangle.docx
Quality Assurance Triangle.docxQuality Assurance Triangle.docx
Quality Assurance Triangle.docxPALKAMITTAL
 
Quality management assignment
Quality management assignmentQuality management assignment
Quality management assignmentselinasimpson331
 
Quality management distance learning
Quality management distance learningQuality management distance learning
Quality management distance learningselinasimpson3001
 
Implementing quality management system
Implementing quality management systemImplementing quality management system
Implementing quality management systemselinasimpson341
 
Ranga Ramanujam Performance Measurement Slides
Ranga Ramanujam Performance Measurement SlidesRanga Ramanujam Performance Measurement Slides
Ranga Ramanujam Performance Measurement SlidesShawnHoke
 
PUB 611Seminar in Public Human Resources Administration Questions &.docx
PUB 611Seminar in Public Human Resources Administration Questions &.docxPUB 611Seminar in Public Human Resources Administration Questions &.docx
PUB 611Seminar in Public Human Resources Administration Questions &.docxwoodruffeloisa
 
Roles and Functions in Controlling
Roles and Functions in ControllingRoles and Functions in Controlling
Roles and Functions in ControllingMaria Neze Dalimocon
 
Mustafa Degerli - 2012 - SEPG EUROPE 2012 - Poster - Factors Influencing the ...
Mustafa Degerli - 2012 - SEPG EUROPE 2012 - Poster - Factors Influencing the ...Mustafa Degerli - 2012 - SEPG EUROPE 2012 - Poster - Factors Influencing the ...
Mustafa Degerli - 2012 - SEPG EUROPE 2012 - Poster - Factors Influencing the ...Dr. Mustafa Değerli
 
Evaluating Collaborative Filtering Recommender Systems
Evaluating Collaborative Filtering Recommender SystemsEvaluating Collaborative Filtering Recommender Systems
Evaluating Collaborative Filtering Recommender SystemsMegaVjohnson
 
Training on the topic MSA as per new RevAF.pptx
Training on the topic MSA as per new RevAF.pptxTraining on the topic MSA as per new RevAF.pptx
Training on the topic MSA as per new RevAF.pptxSantoshKale31
 

Similar to Ppt evaluation of information retrieval system (20)

Data collection and reporting of key performance indicators
Data collection and reporting of key performance indicatorsData collection and reporting of key performance indicators
Data collection and reporting of key performance indicators
 
Program evaluation
Program evaluationProgram evaluation
Program evaluation
 
Evaluation of an Information System.pptx
Evaluation of an Information System.pptxEvaluation of an Information System.pptx
Evaluation of an Information System.pptx
 
Control
ControlControl
Control
 
Example of quality management system
Example of quality management systemExample of quality management system
Example of quality management system
 
Health Informatics- Module 4-Chapter 1.pptx
Health Informatics- Module 4-Chapter 1.pptxHealth Informatics- Module 4-Chapter 1.pptx
Health Informatics- Module 4-Chapter 1.pptx
 
Default Credit Loss
Default Credit LossDefault Credit Loss
Default Credit Loss
 
Quality Assurance Triangle.docx
Quality Assurance Triangle.docxQuality Assurance Triangle.docx
Quality Assurance Triangle.docx
 
Quality management assignment
Quality management assignmentQuality management assignment
Quality management assignment
 
B05110409
B05110409B05110409
B05110409
 
Quality management distance learning
Quality management distance learningQuality management distance learning
Quality management distance learning
 
L2.pptx
L2.pptxL2.pptx
L2.pptx
 
Implementing quality management system
Implementing quality management systemImplementing quality management system
Implementing quality management system
 
Ranga Ramanujam Performance Measurement Slides
Ranga Ramanujam Performance Measurement SlidesRanga Ramanujam Performance Measurement Slides
Ranga Ramanujam Performance Measurement Slides
 
PUB 611Seminar in Public Human Resources Administration Questions &.docx
PUB 611Seminar in Public Human Resources Administration Questions &.docxPUB 611Seminar in Public Human Resources Administration Questions &.docx
PUB 611Seminar in Public Human Resources Administration Questions &.docx
 
Roles and Functions in Controlling
Roles and Functions in ControllingRoles and Functions in Controlling
Roles and Functions in Controlling
 
Mustafa Degerli - 2012 - SEPG EUROPE 2012 - Poster - Factors Influencing the ...
Mustafa Degerli - 2012 - SEPG EUROPE 2012 - Poster - Factors Influencing the ...Mustafa Degerli - 2012 - SEPG EUROPE 2012 - Poster - Factors Influencing the ...
Mustafa Degerli - 2012 - SEPG EUROPE 2012 - Poster - Factors Influencing the ...
 
OEES Glossary
OEES GlossaryOEES Glossary
OEES Glossary
 
Evaluating Collaborative Filtering Recommender Systems
Evaluating Collaborative Filtering Recommender SystemsEvaluating Collaborative Filtering Recommender Systems
Evaluating Collaborative Filtering Recommender Systems
 
Training on the topic MSA as per new RevAF.pptx
Training on the topic MSA as per new RevAF.pptxTraining on the topic MSA as per new RevAF.pptx
Training on the topic MSA as per new RevAF.pptx
 

More from silambu111

More from silambu111 (8)

Search engine
Search engineSearch engine
Search engine
 
Sony
SonySony
Sony
 
Evaluation of medlars
Evaluation of medlarsEvaluation of medlars
Evaluation of medlars
 
Precis
PrecisPrecis
Precis
 
POPSI
POPSIPOPSI
POPSI
 
Citation indexing
Citation indexingCitation indexing
Citation indexing
 
Mam assign
Mam assignMam assign
Mam assign
 
Airtel
AirtelAirtel
Airtel
 

Recently uploaded

Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Bhuvaneswari Subramani
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdfSandro Moreira
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodJuan lago vázquez
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfsudhanshuwaghmare1
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoffsammart93
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...apidays
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingEdi Saputra
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native ApplicationsWSO2
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...apidays
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...apidays
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWERMadyBayot
 
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...apidays
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Jeffrey Haguewood
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontologyjohnbeverley2021
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxRemote DBA Services
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024The Digital Insurer
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2
 

Recently uploaded (20)

Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
 
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
 

Ppt evaluation of information retrieval system

  • 1. Submitted by Somipam R. Shimray Iii/semester dept. of LIS Pondicherry University
  • 2. INTRODUCTION  Evaluation is a systematic determination of a subject's merit, worth and significance, using criteria governed by a set of standards.  It ascertain the degree of achievement in regard to the aim and objectives and results of any such action that has been completed.  Evaluation of information retrieval system measure which of the two existing system perform better and try to assess how the level of performance of a given can be improved.  Lancaster state that evaluation of information retrieval system can be justified by the following three issues:  How well the system is satisfying its objectives  How efficiently it is satisfying its objectives and  Whether the system justified its existence.
  • 3. PURPOSE OF EVALUATION  The main purpose of the evaluation is to focus on the process of implementation rather than on its impact.  Evaluation studies also investigate the degree to which the state goals have been achieved to which these can be achieved.  To measure information retrieval effectiveness in the standard way, we need a test collection consisting of three things:  A document collection.  A test suite of information needs, expressible as queries.  A set of relevance judgments, standardly a binary assessment of either relevant or non relevant for each query-document pair.
  • 4. PURPOSE OF EVALUATION Swanson state seven purposes for evaluation:  To assess a set of goals, a programme plan, or a design prior to implementation.  To determine whether and how well goals or performance expectation are being fulfilled.  To determine specific reasons for success and failure.  To uncover principles underlying a successful programme.  To explore technique for increasing programme effectiveness.  To established a foundation of further research on the reason for the relative success of alternative technique and  To improve the means employed for attaining objectives or to redefine sub goals or goals in view of research findings.
  • 5. PURPOSE OF EVALUATION Keen give three major purpose of evaluation for an information retrieval system:  The need for measures with which to make merit comparisons within a single test situation. In other words, evaluation studies are conducted to compare the merits or demerits of two or more system.  The need for measure with which to make comparison between results obtained in different test situation and  The need for assessing the merit of a real-life system.
  • 6. EVALUATION CRITERIA  Evaluation of Information Retrieval can be conduct into two different viewpoints.  Managerial viewpoint: when evaluation is conducted from managerial point of view it is called managerial oriented evaluation.  User viewpoint: when evaluation is conducted from the user point of view it is called user-oriented evaluation study.
  • 7. EVALUATION CRITERIA Lancaster in 1971 proposed five Evaluation Criteria  Coverage of the system  Ability of the system to retrieve wanted items (i.e. recall).  Ability of the system to avoid retrieval of unwanted items (i.e. precision).  The response time of the system and  The amount of effort required by the user.
  • 8. EVALUATION CRITERIA  Recall  Precision  Fallout  Generality
  • 9. EVALUATION CRITERIA Recall  Recall is defined as the proportion of the total relevant documents that is retrieved. Number of relevant item retrieved Recall=——————————————————————x 100 Total number of relevant items in the collection  Example If there are 100 documents in a collection that are relevant to a given query and 60 of these items are retrieved in a given search, then the recall is state to be 60% in other words the system has been able to retrieve 60% of the relevant items.
  • 10. 60 Recall= ———x 100 100 Recall= 60% Total number of relevant items in the collection=100 Number of relevant item retrieved=60 Recall Total no. of relevent items no. of relevent items
  • 11. EVALUATION CRITERIA Precision  Precision is defined as the proportion of documents retrieved that is relevant. Number of relevant item retrieved Precision=——————————————————x 100 Total number of items retrieved  Example In a given search the system retrieves 80 items, out of which 40 are relevant and 40 are non-relevant, the precision is 50%.
  • 12. 40 Precision= —————x 100 80 Precision= 50% Total number of items retrieved=80 Number of relevant item retrieved=40 Precision Total no. of relevent items in the collection Total no. of items retrieved No. of relevent items retrieved
  • 13. Recall-Precision matrix R= [a/ (a+c)] x 100 P= [a/ (a+b)] x 100 Relevant Not-relevant Total Retrieved a (hints) b (noise) a+b Not-retrieved c (misses) d rejected c+d Total a+c b+d a+b+c+d
  • 14. EVALUATION CRITERIA Fallout  Fallout ratio is the proportion of non-relevant items that has been retrieved in a given search. Generality  Generality ratio is the relevant items that have been retrieved in a given search.
  • 15. Retrieval Measure Symbol Evaluation measure Formula Explanation R Recall a/(a+c) Proportion of relevant items retrieved P Precision a/(a+b) Proportion of retrieved item that are relevant F Fallout b/(b+d) Proportion of non-relevant items retrieved G Generality (a+c)/(a+b+c+d) Proportion of relevant items per query
  • 16. LIMITATIONS OF RECALL AND PRECISION  Different users may want different levels of recall.  A person going to prepare a state-of-the-art report on a topic would like to have all the items available on the topics and therefore will go for high recall.  Where as a user wanting to know about a given topic will prefer to have a few items and thus will not require a high recall.
  • 17. OTHER EVALUATION CRITERIA  Effectiveness  Efficiency  Usability  Satisfaction  Cost
  • 18. OTHER EVALUATION CRITERIA Effectiveness  Effectiveness means the level up to which the given system attained its objectives.  In an information retrieval system effectiveness may be measure of how far it can retrieve relevant information while with-holding non- relevant information. Efficiency  Efficiency means how economically the system is achieving its objectives.  In an information retrieval system efficiency can be measured be factor such as cost.  Cost include factor such as response time, time taken by the system to provide an answer.
  • 19. OTHER EVALUATION CRITERIA Usability  Measure that embraces the interface through which the user interacts with the system.  Takes into account the user and their expectations, skills and experiences. Satisfaction  Search task  Search setting  The searcher’s state contributes to quality and satisfaction judgments in digital environments.  Other perspectives on satisfaction are to be found in the service quality and website quality literatures.
  • 20. OTHER EVALUATION CRITERIA Cost  Users may experience costs in terms of any payment that they need to make for system or document access.  Most significant cost is associated with the time that they spend in searching a system.
  • 21. CONCLUSION  Evaluation is a systematic determination of a subject's merit, worth and significance, using criteria governed by a set of standards.  Effectiveness and efficiency are two basic parameter for measuring the performance of system.  Evaluation criteria  Managerial viewpoint: when evaluation is conducted from managerial point of view it is called managerial oriented evaluation.  User viewpoint: when evaluation is conducted from the user point of view it is called user-oriented evaluation study.  Recall, Precision, Fallout and Generality.