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7/24/2019 Ph.D. Viva Presentation
1/71
Ph.D.Viva-Voce
VENINGSTONK
Objectives of
ResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligence
Model forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Personalized Information Retrieval SystemUsing Computational Intelligence Techniques
VENINGSTON K
Senior Research FellowDepartment of Computer Science and Engineering
Government College of Technology, [email protected]
Under the Guidance ofDr.R.SHANMUGALAKSHMI
Associate Professor, Dept. of CSE, GCT
05 August 2015
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 1 / 71
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Ph.D.Viva-Voce
VENINGSTONK
Objectives of
ResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligence
Model forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Presentation Outline
1 Objectives of Research Work
2 Introduction
3 Literature Survey
4 Proposed Research Works
Term Association Graph Model for Document Re-rankingTopic Model for Document Re-rankingGenetic Intelligence Model for Document Re-rankingSwarm Intelligence Model for Search Query Reformulation
5 Conclusion
6 References
7 Publications
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 2 / 71
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Ph.D.Viva-Voce
VENINGSTONK
Objectives of
ResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligence
Model forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Objectives of Research Work
To improve the retrieval effectiveness by employing TermAssociation Graph data structure
To enhance a personalized ranking criteria by modeling of
users search interests as topics. Further, employingDocument topic model that integrates User topic model
To realize Genetic Algorithm enabled document re-rankingscheme
To devise personalized search query suggestion using AntColony Optimization
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 3 / 71
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VENINGSTONK
Objectives of
ResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligence
Model forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Introduction [1/2]
Typical Information Retrieval (IR) Architecture
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VENINGSTONK
Objectives of
ResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligence
Model forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Introduction [2/2]
WhyPersonalizationin Information Retrieval?
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VENINGSTONK
Objectives of
ResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligence
Model forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Classifications of Typical IR systems
Content-based approachSimple matchingof a query with results - This does not helpusers to determine which results are worth
Author-relevancy technique
Citation and hyperlinks - Presents the problem ofauthoringbiasi.e. results that are valued by authors are not necessarilythose valued by the entire population
Usage rank approachActions of users to compute relevancy - Computed from thefrequency, recency, duration of interaction by users
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Ph.D.Viva-Voce
VENINGSTONK
Objectives of
ResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligence
Model forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Limitations in Typical IR systems
Most of the techniques measure relevanceas a function ofthe entire population of users
This does not acknowledge thatrelevance is relativefor
each userThere needs to be a way to take into account thatdifferent people find different things relevant
Users interests and knowledge change over time -
personal relevance
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VENINGSTONK
Objectives of
ResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligence
Model forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
General Approach for mitigating Challenges
Main ways to personalize a search are Result processingandQuery augmentation
Document Re-ranking
To re-rank the results based upon the frequency, recency, orduration of usage. Provides users with the ability to identifythe most popular, faddish pages that other users have seen
Query Reformulation
To compare the entered query against the contextualinformation available to determine if the query can berefined/reformulated to include other text
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VENINGSTONK
Objectives of
ResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligence
Model forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
General Problem Description
Diverse interest of search usersOriginal Query User 1 User 2 User 3
World cup football championship ICC cricket world cup T20 cricket world cup
India crisis Economic crisis in India security crisis in India job crisis in India
Job search Student part time jobs government jobs Engineering and IT job search
Cancer astrology and zodiac lung cancer and prevention causes of cancer, symptoms and treatment
Ring Ornament horror movie circus ring show
Okapi animal giraffe African luxury hand bags Information retrieval model BM25
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VENINGSTONK
Objectives of
ResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Literature Survey
Related work on Re-ranking techniquesPaper Title Author, Year Techniques used Limitations
Implicit preference Sugiyama et al, 2004 Term frequency scheme Noisy browsing history
Hyp erlink data Brin & Page, 1998 Link structure analys is Computes universal notion of imp ortance
Collab orative filtering Sarwar et al, 2000 Groupization algorithm User data are dynamic
Categorization Liu et al, 2004 Mapping queries to related categories Predefined categories are used
Long-term user behavior Bennett et al, 2012 Create profiles from entire history Misses searcher needs for the current task
Short-term user behavior Cao et al, 2008 Create profiles from recent search session Lacks in capturing users long term interest
Location awarenes s Leung et al, 2010 Location ontology Captures lo cation information by text matching
Task awareness Luxenburger et al, 2008 Task language mo del Lacks tem poral features of user tasks
Tag data Carman et al, 2008 Content based profiles Results are biased towards particular user group
Context data White etal,2009 Usermodeling uses Contextual features Treat all context sources equally
Click data Liu et al, 2002 Assesses pages frequently clicked Makes no use of terms and its asso ciation
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VENINGSTONK
Objectives of
ResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Literature Survey
Related work on Query reformulation techniquesPaper Title Author, Year Techniques used Limitations
Anchor text Kraft & Zien, 2004 Mining anchor texts More number of query suggestions
Bipartite graph Mei et al, 2008 Prepares morphological different keywords Individual user intents are not considered
Personalized facets Koren et al, 2008 Employs key-value pair meta-data Uses frequency based facet ranking
Term association pattern Wang & Zhai, 2008 Analyze relations of terms inside a query Click-through data not considered
Rule based classifier Huang & Efthimiadis, 2009 Matches query with ordered reformulation rules Semantic associations are missing
Clustering Jain & Mishne, 2010 Query suggestions are grouped by topics Drift in user intent to another topic
Merging Sheldon et al, 2011 Produces results from different reformulations Random walk on the click graph
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VENINGSTONK
Objectives of
ResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Proposed Research Works
Module 1Term Association Graph Model for Document Re-ranking
Module 2
Topic Model for Document Re-ranking
Module 3
Genetic Intelligence Model for Document Re-ranking
Module 4
Swarm Intelligence Model for Search Query Reformulation
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VENINGSTONK
Objectives of
ResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
1. Term Association Graph Model for DocumentRe-ranking
Problem Statement
How to represent document collection as term graphmodel?
How to use it for improving search results?
Methodology
Term graph representation
Ranking semantic association for Re-ranking
TermRank based approach (TRA)
Path Traversal based approach (PTA)1 PTA1: Naive approach2 PTA2: Paired similarity document ordering3 PTA3: Personalized path selection
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VENINGSTONK
Objectives of
ResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Document Representation
Sample of OHSUMED (Oregon Health & Science
University MEDline) test CollectionDocID Item-set Support
54711 Ribonuclease, catalytic, lysine, phosphate, enzymatic, ethylation 0.12
55199 Ribonuclease, Adx, glucocorticoids, chymotrypsin, mRNA 0.2
62920 Ribonuclease, anticodon, alanine, tRNA 0.1
64711 Cl- channels, catalytic, Monophosphate, cells 0.072
65118 isozyme, enzyme, aldehyde, catalytic 0.096
Supportd=
ni=1fd(ti)N
j=1
ni=1fd(ti)
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VENINGSTONK
Objectives ofResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Term Association Graph Model
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Ph D
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Objectives ofResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Ranking Schemes based on Semantic Association
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Ph D T R k A h (TRA)
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Objectives ofResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel forDocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Term Rank Approach (TRA)
Rank(ta) =c
tbTa
Rank(tb)Ntb
ta and tbare Nodes
Tb is a set of terms ta points to
Ta is a set of terms that point to ta
Ntb=|Tb| is the number of links from ta
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Ph D PTA1 N i A h
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VENINGSTONK
Objectives ofResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
PTA1: Naive Approach
The sequence of documents are chosen from path p3 i.e.D11,D1,D37,D17,D22, andD5. D11 will be the top rankeddocument.
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Ph D PTA2 P i d Si il i R ki
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Objectives ofResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
PTA2: Paired Similarity Ranking
sim(T1,T2) = 2 depth(LCS)
depth(T1)+depth(T2)
T1 and T2 denote the term nodes in Term AssociationGraph TGLCSdenote the Least Common Sub-Sumer ofT1 and T2depth(T) denote the shortest distance from query node qto a node T on TG
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Ph D PTA3 P li d P th S l ti
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VENINGSTONK
Objectives ofResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
PTA3: Personalized Path Selection
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Ph D PTA3 P li d P th S l ti
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VENINGSTONK
Objectives ofResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
PTA3: Personalized Path Selection
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Ph.D. PTA3: Personalized Path Selection
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Viva-Voce
VENINGSTONK
Objectives ofResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
PTA3: Personalized Path Selection
PSCweight= 1
|t|
#topics
i=1
(sivi(
tTi))
1
#t /T
|t|
PSCweight is the Personalized Search Context Weight
|t| is the total number of terms in dfspath includingquery term
T is the set of user interested topics
sivi is the search interest value of ith topic
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Ph.D. Experimental Dataset
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VENINGSTONK
Objectives ofResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Experimental Dataset
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Ph.D. Evaluation Measures
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VENINGSTONK
Objectives ofResearchWork
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Evaluation Measures
Subjective Evaluation
1 Information Richness
InfoRich(Rm) = 1
Div(Rm)
Div(Rmk=1
1
Nk
Nki=1
InfoRich(dik)
Objective Evaluation1 Precision P= #RelevantRetrived
k
2 Recall P= #RelevantRetrievedTotal#Relevant
3 Mean Average PrecisionMAP=|Q|
q=1AvgPrecision(q)
|Q|
AvgPrecision(q) = 1RR
k=1((P@k) . (rel(k)))4 Mean Reciprocal RankMRR= 1|Q|
|Q|i=1
1ranki
5 Normalized Discounted Cumulative Gain
NDCGk=k
i=12ri1
log2(i+1)
IDCGk
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Ph.D. Experimental Results & Analysis [1/3]
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VENINGSTONK
Objectives ofResearch
Work
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Experimental Results & Analysis [1/3]
Non-Personalized Evaluation on Real Dataset
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Ph.D.V V Experimental Results & Analysis [2/3]
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Viva-Voce
VENINGSTONK
Objectives ofResearch
Work
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Experimental Results & Analysis [2/3]
Non-Personalized Evaluation on Synthetic Dataset
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Ph.D.Vi V Experimental Results & Analysis [3/3]
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Viva-Voce
VENINGSTONK
Objectives ofResearch
Work
Introduction
LiteratureSurvey
ProposedResearch
WorksTermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Experimental Results & Analysis [3/3]
Personalized Evaluation on Real Dataset
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Ph.D.Vi V e Motivation to Module 2
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VENINGSTONK
Objectives ofResearch
Work
Introduction
LiteratureSurvey
ProposedResearchWorks
TermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Motivation to Module 2
Summary of Module 1
1 Employs termassociation graphmodel
2 Suggested differentmethods to enhancethe documentre-ranking
3 Captures hiddensemantic association
Exploits topical representationfor identifying user interest.Matching of documents and
queries is not done with topicalrepresentation. To explore topicmodel to find relevantdocuments by matching topicalfeatures
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Ph.D.Viva Voce 2 Topic Model for Document Re-ranking
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VENINGSTONK
Objectives ofResearch
Work
Introduction
LiteratureSurvey
ProposedResearchWorks
TermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
2. Topic Model for Document Re ranking
Problem Statement
How to model and represent past search contexts?
How to use it for improving search results?
Methodology
User search context modeling
1 User profile modeling2 Learning user interested topic3 Finding document topic
Personalized Re-ranking process
1 Exploiting user interest profile model2 Computing personalized score for document using usermodel
3 Generating personalized result set by re-ranking
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Ph.D.Viva-Voce User search context modeling
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VENINGSTONK
Objectives ofResearch
Work
Introduction
LiteratureSurvey
ProposedResearchWorks
TermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
User search context modeling
User profile modeling
u=UPwi
UPwiHistory(D)=P(wi) = tfwi,D
wiD
tfwi,D
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Ph.D.Viva-Voce User search context modeling
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Viva Voce
VENINGSTONK
Objectives ofResearch
Work
Introduction
LiteratureSurvey
ProposedResearchWorks
TermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
User search context modeling
Learning user interested topic
Finding document topic
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Ph.D.Viva-Voce Personalized Re-ranking process
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Viva Voce
VENINGSTONK
Objectives ofResearch
Work
Introduction
LiteratureSurvey
ProposedResearchWorks
TermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
g p
Exploiting user interest profile model
KLD(TdTu) = tDUP(Td(t))log
P(Td(t))
P(Tu(t))
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Objectives ofResearch
Work
Introduction
LiteratureSurvey
ProposedResearchWorks
TermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
g p
Computing personalized score for document using user
model
P(D |Q, u) =(D |u)P(Q |D, u)
P(Q |u)
P(Q |D, u) =P(Q |Td,Tu)+qiQ
(P(qi |u)+(1)P(qi |D)
P(Q |Tu,Td) =qiQ
(P(qi |Tu) + (1)P(qi |Td))
Generating personalized result set by re-ranking1 The documents are scored based on P(Q |D, u)2 Result set is re-arranged based on descending order of the
personalized score
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Ph.D.Viva-Voce Experimental Dataset
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Objectives ofResearch
Work
Introduction
LiteratureSurvey
ProposedResearchWorks
TermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel for
DocumentRe-ranking
SwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
p
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Ph.D.Viva-Voce Parameter Tuning
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g
Learning and Parameters
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[ / ]
Evaluation on Real Dataset
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Evaluation on AOL Dataset
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Ph.D.Viva-Voce Motivation to Module 3
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Summary of Module 2
1 Client sidepersonalization
2 Insensitive to the
number of Topics3 Not all the queries
would requirepersonalization to be
performed
Explores topic model for findingrelevant documents using
topical features. To learn atopic model on a representativesubset of a collection usingGenetic Intelligence technique
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Ph.D.Viva-Voce 3. Genetic Intelligence Model for Document
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Publications
Re-ranking
Problem StatementHow to represent documents as chromosomes?
How to evaluate fitness of search results?
Methodology
Apply GA with an adaptation of probabilistic modelProbabilistic similarity function has been used for fitnessevaluation
Documents are assigned a score based on the probability
of relevanceProbability of relevance are sought using GA approach inorder to optimize the search process i.e. finding of relevantdocument not by assessing the entire corpus or collection
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Ph.D.Viva-Voce Why GA for IR?
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When the document search space represents a highdimensional space i.e. the size of the document corpus ismultitude in IR
GA is the searching mechanisms known for itsquick search
capabilitiesWhen no relevant documents are retrieved in top orderwith the initial query
Theprobabilistic explorationinduced by GA allows the
exploration of new areas in the document space.
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Ph.D.Viva-Voce Steps in GA for IR
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PublicationsVENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 41 / 71
Ph.D.Viva-Voce Fitness Evaluation
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Representations of Chromosomes
Probabilistic Fitness Functions
1 P(q|d) =
wd(P(q|w)P(w |d))
2 P(q|d) =P(q|C) + (1)
wd(P(q|w)P(w |d))
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Ph.D.Viva-Voce Selection & Reproduction
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Publications
Roulette-wheel selection
Reproduction Operators1 Crossover2 Mutation
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Experimental Dataset
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Parameter Tuning
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LearningPc and Pm Parameters
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Experimental Results & Analysis [1/2]
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Evaluation on Benchmark Dataset
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Evaluation on Real Dataset
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Summary of Module 3
1 Explored the utility ofincorporating GA toimprove re-ranking
2 Adaptation of
personalization in GAprovides more desirableresults
3 Not all the querieswould requirepersonalization to beperformed
The graph representation ofdocuments best suit theapplication of SwarmIntelligence model. To simulateACO in graph structure basedon behavior of ants seeking apath between their colony andsource of food for search query
reformulation
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4. Swarm Intelligence Model for Search QueryReformulation
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Reformulation
Problem Statement
How to address vocabulary mismatch problem in IR?
How to change the original query to form a new querythat would find better relevant documents?
Methodology
Exploits Ant Colony Optimization (ACO) approach tosuggest related key words
The self-organizing principles which allow the highlycoordinated behavior of real ants that collaborate to solvecomputational problems
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ACO for Query Reformulation
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Terminologies
Artificial Ant
Pheromone
Typical Ant SystemVENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 50 / 71
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PublicationsVENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 51 / 71
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Characteristics of Artificial Ant
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Notion ofautocatalytic behavior
Chooses the query term to go with a transition probabilityas a function of the similarity i.e. amount of trail presenton the connecting edge between terms
Navigation over retrieved documents for a query is treatedas ant movement over graph
When the user completes a tour, a substance calledtrailortraceorpheromoneis laid on each edge
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Similarity
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Transition Probability
pkij(t) = [ij(t)]
[ij]
k[ik(t)][ik]
ij is a static similarity weight
ij is a trace deposited by usersTrail Deposition
ij(t+ 1) =pij(t) + ij
pis the rate of trail decay per time interval i.e. pheromoneevaporation factor
ij is the sum of deposited trails by users
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AOL Search query log
Only the queries issued by at least 10 users were employedand the pre-processed documents retrieved for that querywere used to construct graph
270 single and two word queries issued by different usersfrom AOL search log are taken
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Baseline MethodsAssociation Rule based approach (AR)
SimRank Approach (SR)
Backward Random Walk approach (BRW)
Forward Random Walk approach (FRW)Traditional ACO based approach (TACO)
Parameter setting
Depth was set as 5 i.e. top ranked 5 related queries
Evaporation factor (p) was set to 0.5
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Manual Evaluation
Benchmark Evaluation
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Summary of Module 4
1 Terms in the initial set of documents constitute potential
related terms2 Semantically related keywords are suggested to the initial
query
3 Single word queries are treated as an ambiguous one
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1 Usage of Term Association GraphEfficient retrieval of Journal articles
The graph structure may signify grammatical relationsbetween terms
2 Integration of Document Topic model and User Topicmodel
Effective in general searchThis may incorporate live user feedback
3 GA based document fitness evaluationGood in document space explorationChromosomes representation may be improved
4 ACO based query reformulationExploits collaborative knowledge of usersIf the solution is badly chosen, the probability of a badperformance is high
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1 Efficient updating policy for user interest models
2
Account individual user specific context for generatingquery refinements
3 Medical Information Retrieval (Eg. PubMed, WebMD,etc.)
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Book References
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References
Publications
Salton & McGill (1986)
Introduction to modern information retrieval
McGraw-Hill , New York.
Baeza-Yates & Ribeiro-Neto (1999)
Modern Information Retrieval
Addison Wesley.
Manning et al (2008)
Introduction to Information Retrieval
Cambridge University Press .
Goldberg (1989)Genetic Algorithms in Search, Optimization, and Machine Learning
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References [1/7]
M tthiji & R dli ki (2012)
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References
Publications
Matthijis & Radlinski (2012)
Personalizing Web Search using Long Term Browsing History
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Agichtein et al (2006)
Improving Web Search Ranking by Incorporating user behaviorinformation
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Ponte & Croft (1998)A language modeling approach to information retrieval
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Lafferty et al (2001)
Document language models, query models, and risk minimization for
information retrievalIn Proc. 24th ACM SIGIR, 111 119.
Kushchu (2005)
Web-Based Evolutionary and Adaptive Information Retrieval
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Leung & Lee (2010)
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Conclusion
References
Publications
Leung & Lee (2010)
Deriving Concept-based User profiles from Search Engine Logs
IEEE Trans. Knowledge and Data Engineering22(7), 969 982.
Blanco & Lioma (2012)
Graph-based term weighting for information retrieval
Springer Information Retrieval15(1), 54 92.
Dorigo et al (2006)
Ant Colony OptimizationIEEE Computational Intelligence Magazine1(4), 28 39.
Sugiyama et al (2004)
Adaptive web search based on user pro?le constructed without anyeffort from users
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Brin Page (1998)
The Anatomy of a Large-Scale Hypertextual Web Search Engine
Elsevier Journal on Computer Networks and ISDN Systems30(1-7),107 117.
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References [3/7]
Eirinaki Vazirgiannis (2005)
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Conclusion
References
Publications
Eirinaki Vazirgiannis (2005)
UPR:Usage-based page ranking for web persoanalization
In Proc. 5th IEEE Intl. Conf. Data Mining, 130 137.
Sarwar et al (2000)
Analysis of Recommendation Algorithms for E-commerce
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Liu et al (2004)
Personalized web search for improving retrieval effectiveness
IEEE Trans. Knowledge and Data Engineering16(1), 28 40.
Bennett et al (2012)
Modeling the impact of short- and long-term behavior on search
personalizationIn Proc. 35th ACM SIGIR, 185 194.
cao et al (2008)
Context-Aware Query Suggestion by Mining Click-Through
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( )
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Conclusion
References
Publications
carman et al (2008)
Tag data and personalized Information Retrieval
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Leung et al (2010)
Personalized Web Search with Location Preferences
In Proc. 26th IEEE Intl. Conf. Data Engineering, 701 712.
Luxenburger et al (2008)Task-aware search personalization
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white et al (2009)
Predicting user interests from contextual information
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White et al (2013)
Enhancing personalized search by mining and modeling task behavior
In Proc. 22nd Intl. Conf. World Wide Web, 1411 1420.
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Vallet et al (2010)
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Conclusion
References
Publications
Vallet et al (2010)
Personalizing web search with Folksonomy-Based user and documentprofiles
In Proc. 32nd European conference on Advances in IR, 420 431.
Jansen et al (2007)
Determining the user intent of web search engine queries
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Jansen et al (2000)Real life, real users, and real needs: a study and analysis of userqueries on the web
Elsevier Information Processing and Management36(2), 207 227.
Daoud et al (2008)
Learning user interests for a session-based personalized SearchIn Proc. 2nd Intl. symposium on Information interaction in context,57 64.
Kraft Zien (2004)
Mining Anchor Text for Query Refinement
In Proc. 13th Intl. Conf. WorldWide Web, 666 674.VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 65 / 71
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References [6/7]
Dang Croft (2010)
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TermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
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Conclusion
References
Publications
Dang Croft (2010)
Query Reformulation Using Anchor Text
In Proc. 3rd ACM WSDM, 41 50.
Mei et al (2008)
Query suggestion using hitting time
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Koren et al (2008)
Personalized interactive faceted searchIn Proc. 17th Intl. Conf. World Wide Web, 477 486.
Wang Zhai (2005)
Mining term association patterns from search logs for effective queryReformulation
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Huang Efthimiadis (2009)
Analyzing and evaluating query reformulation strategies in web searchlogs
In Proc. 18th ACM CIKM, 77 86.
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Jain Mishne (2010)
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TermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
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Conclusion
References
Publications
Jain Mishne (2010)
Organizing query completions for web search
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Sadikov et al (2010)
Clustering query refinements by user intent
In Proc. 19th Intl. Conf. World Wide Web, 841 850.
Bhatia (2011)
Query suggestions in the absence of query logs
In Proc. 34th ACM SIGIR, 795 804.
Sheldon et al (2011)
LambdaMerge: Merging the results of query reformulations
In Proc. 4th ACM WSDM, 117 125.
Goyal et al (2012)
Query representation through lexical association for informationretrieval
IEEE Trans. Knowledge and Data Engineering24(12),22602273.
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Journal Publications
Veningston, & Shanmugalakshmi (2015)
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References
Publications
g , & g ( )
Semantic Association Ranking Schemes for Information RetrievalApplications using Term Association Graph Representation
Sadhana - Academy Proceedings in Engineering Sciences, SpringerPublication.[Annexure I]
Veningston & Shanmugalakshmi (2014)
Computational Intelligence for Information Retrieval using GeneticAlgorithm
INFORMATION - An International Interdisciplinary Journal17(8),3825 3832.[Annexure I]
Veningston & Shanmugalakshmi (2014)
Combining User Interested Topic and Document Topic forPersonalized Information Retrieval
Lecture Notes in Computer ScienceSpringer Publication 8883 , 60 79.[Annexure II]
Veningston & Shanmugalakshmi (2014)
Efficient Implementation of Web Search Query reformulation usingAnt Colony Optimization
Lecture Notes in Com uter Science S rin er Publication 8883 80 VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 68 / 71
Ph.D.Viva-Voce
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International Conference Publications [1/2]
Veningston, & Shanmugalakshmi (2015)
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69/71
Objectives ofResearchWork
Introduction
LiteratureSurvey
ProposedResearchWorks
TermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel forDocument
Re-rankingSwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
g , g ( )
Personalized Location aware Recommendation System
In Proc. 2nd IEEE Intl. Conf. Advanced Computing and
Communication Systems , Indexed in IEEE Xplore.[Best Paper]
Veningston & Shanmugalakshmi (2014)
Information Retrieval by Document Re-ranking using TermAssociation Graph
In Proc. ACM Intl. Conf. Interdisciplinary Advances in Applied
Computing, Indexed in ACM Digital Library.[Best Paper]
Veningston & Shanmugalakshmi (2014)
Personalized Grouping of User Search Histories for Efficient WebSearch
In Proc. 13th WSEAS Intl. Conf. Applied Computer and Applied
Computational Science, 164 172.
Veningston & Shanmugalakshmi (2013)
Statistical language modeling for personalizing Information Retrieval
In Proc. 1st IEEE Intl. Conf. Advanced Computing and
Communication Systems , Indexed in IEEEXplore.[BestPaper]VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 69 / 71
Ph.D.Viva-Voce
VENINGSTONK
International Conference Publications [2/2]
http://find/7/24/2019 Ph.D. Viva Presentation
70/71
Objectives ofResearchWork
Introduction
LiteratureSurvey
ProposedResearchWorks
TermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel forDocument
Re-rankingSwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Veningston, Shanmugalakshmi & Ruksana (2013)
Context aware Personalization for Web Information Retrieval: A Largescale probabilistic approach
In Proc. Intl. Conf. Cloud and Big Data Analytics , PSG College ofTechnology.
Veningston & Shanmugalakshmi (2012)
Enhancing personalized web search Re-ranking algorithm byincorporating user profile
In Proc. 3rd IEEE Intl. Conf. Computing, Communication and
Networking Technologies , Indexed in IEEE Xplore.
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 70 / 71
Ph.D.Viva-Voce
VENINGSTONK
http://find/7/24/2019 Ph.D. Viva Presentation
71/71
Objectives ofResearchWork
Introduction
LiteratureSurvey
ProposedResearchWorks
TermAssociationGraph Model forDocumentRe-ranking
Topic Model forDocumentRe-ranking
GeneticIntelligenceModel forDocument
Re-rankingSwarmIntelligenceModel for SearchQueryReformulation
Conclusion
References
Publications
Thank You & Queries
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 71 / 71
http://find/