The document presents research on developing a personalized information retrieval system using computational intelligence techniques. It discusses four proposed models: 1) a term association graph model for document re-ranking, 2) a topic model for document re-ranking, 3) a genetic intelligence model for document re-ranking, and 4) a swarm intelligence model for search query reformulation. The objectives are to improve retrieval effectiveness using term graphs and enhance personalized ranking using user topic modeling. Computational techniques like genetic algorithms and ant colony optimization will be used to re-rank documents and reformulate queries.

1. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Personalized Information Retrieval System
Using Computational Intelligence Techniques
VENINGSTON K
Senior Research Fellow
Department of Computer Science and Engineering
Government College of Technology, Coimbatore
veningstonk@gct.ac.in
Under the Guidance of
Dr.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

2. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
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-ranking
Topic Model for Document Re-ranking
Genetic Intelligence Model for Document Re-ranking
Swarm 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

3. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Objectives of Research Work
To improve the retrieval effectiveness by employing Term
Association Graph data structure
To enhance a personalized ranking criteria by modeling of
user’s search interests as topics. Further, employing
Document topic model that integrates User topic model
To realize Genetic Algorithm enabled document re-ranking
scheme
To devise personalized search query suggestion using Ant
Colony Optimization
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 3 / 71

4. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Introduction [1/2]
Typical Information Retrieval (IR) Architecture
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 4 / 71

5. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Introduction [2/2]
Why Personalization in Information Retrieval?
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 5 / 71

6. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Classifications of Typical IR systems
Content-based approach
Simple matching of a query with results - This does not help
users to determine which results are worth
Author-relevancy technique
Citation and hyperlinks - Presents the problem of authoring
bias i.e. results that are valued by authors are not necessarily
those valued by the entire population
Usage rank approach
Actions of users to compute relevancy - Computed from the
frequency, recency, duration of interaction by users
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 6 / 71

7. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Limitations in Typical IR systems
Most of the techniques measure relevance as a function of
the entire population of users
This does not acknowledge that relevance is relative for
each user
There needs to be a way to take into account that
different people find different things relevant
User’s interests and knowledge change over time -
personal relevance
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 7 / 71

8. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
General Approach for mitigating Challenges
Main ways to personalize a search are Result processing
and Query augmentation
Document Re-ranking
To re-rank the results based upon the frequency, recency, or
duration of usage. Provides users with the ability to identify
the most popular, faddish pages that other users have seen
Query Reformulation
To compare the entered query against the contextual
information available to determine if the query can be
refined/reformulated to include other text
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 8 / 71

9. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
General Problem Description
Diverse interest of search users
Original 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
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 9 / 71

10. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Literature Survey
Related work on Re-ranking techniques
Paper Title Author, Year Techniques used Limitations
Implicit preference Sugiyama et al, 2004 Term frequency scheme Noisy browsing history
Hyperlink data Brin & Page, 1998 Link structure analysis Computes universal notion of importance
Collaborative 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 awareness Leung et al, 2010 Location ontology Captures location information by text matching
Task awareness Luxenburger et al, 2008 Task language model Lacks temporal features of user tasks
Tag data Carman et al, 2008 Content based profiles Results are biased towards particular user group
Context data White et al, 2009 User modeling 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 association
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 10 / 71

11. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Literature Survey
Related work on Query reformulation techniques
Paper 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
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 11 / 71

12. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Proposed Research Works
Module 1
Term 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
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 12 / 71

13. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
1. Term Association Graph Model for Document
Re-ranking
Problem Statement
How to represent document collection as term graph
model?
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 approach
2 PTA2: Paired similarity document ordering
3 PTA3: Personalized path selection
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 13 / 71

14. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Document Representation
Sample of OHSUMED (Oregon Health & Science
University MEDline) test Collection
DocID 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 =
n
i=1 fd (ti )
N
j=1
n
i=1 fd (ti )
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 14 / 71

15. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Term Association Graph Model
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 15 / 71

16. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Ranking Schemes based on Semantic Association
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 16 / 71

17. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Term Rank Approach (TRA)
Rank(ta) = c tb∈Ta
Rank(tb)
Ntb
ta and tb are 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
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 17 / 71

18. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
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 ranked
document.
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 18 / 71

19. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
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 Association
Graph TG
LCS denote the Least Common Sub-Sumer of T1 and T2
depth(T) denote the shortest distance from query node q
to a node T on TG
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 19 / 71

20. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
PTA3: Personalized Path Selection
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 20 / 71

21. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
PTA3: Personalized Path Selection
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 21 / 71

22. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
PTA3: Personalized Path Selection
PSCweight =
1
|t|
#topics
i=1
(sivi ( t ∈ Ti )) ∗ 1 −
#t /∈ T
|t|
PSCweight is the Personalized Search Context Weight
|t| is the total number of terms in dfs–path including
query term
T is the set of user interested topics
sivi is the search interest value of ith topic
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 22 / 71

23. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Experimental Dataset
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 23 / 71

24. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Evaluation Measures
Subjective Evaluation
1 Information Richness
InfoRich(Rm) =
1
Div(Rm)
Div(Rm
k=1
1
Nk
Nk
i=1
InfoRich(di
k )
Objective Evaluation
1 Precision P = #RelevantRetrived
k
2 Recall P = #RelevantRetrieved
Total#Relevant
3 Mean Average Precision MAP =
|Q|
q=1 AvgPrecision(q)
|Q|
AvgPrecision(q) = 1
R
R
k=1 ((P@k) . (rel (k)))
4 Mean Reciprocal Rank MRR = 1
|Q|
|Q|
i=1
1
ranki
5 Normalized Discounted Cumulative Gain
NDCGk =
k
i=1
2ri −1
log2(i+1)
IDCGk
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 24 / 71

25. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Experimental Results & Analysis [1/3]
Non-Personalized Evaluation on Real Dataset
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 25 / 71

26. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Experimental Results & Analysis [2/3]
Non-Personalized Evaluation on Synthetic Dataset
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 26 / 71

27. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Experimental Results & Analysis [3/3]
Personalized Evaluation on Real Dataset
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 27 / 71

28. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Motivation to Module 2
Summary of Module 1
1 Employs term
association graph
model
2 Suggested different
methods to enhance
the document
re-ranking
3 Captures hidden
semantic association
Exploits topical representation
for identifying user interest.
Matching of documents and
queries is not done with topical
representation. To explore topic
model to find relevant
documents by matching topical
features
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 28 / 71

29. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
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 modeling
2 Learning user interested topic
3 Finding document topic
Personalized Re-ranking process
1 Exploiting user interest profile model
2 Computing personalized score for document using user
model
3 Generating personalized result set by re-ranking
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 29 / 71

30. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
User search context modeling
User profile modeling
θu = UPwi
UPwi ∈History(D) = P(wi ) =
tfwi ,D
wi ∈D tfwi ,D
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 30 / 71

31. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
User search context modeling
Learning user interested topic
Finding document topic
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 31 / 71

32. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Personalized Re-ranking process
Exploiting user interest profile model
KLD(Td Tu) =
t∈D∩U
P(Td (t))log
P(Td (t))
P(Tu(t))
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 32 / 71

33. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Personalized Re-ranking process
Computing personalized score for document using user
model
P(D | Q, θu) =
(D | θu)P(Q | D, θu)
P(Q | θu)
P(Q | D, θu) = P(Q | Td , Tu)+
qi ∈Q
(βP(qi | θu)+(1−β)P(qi | D))
P(Q | Tu, Td ) =
qi ∈Q
(αP(qi | Tu) + (1 − α)P(qi | Td ))
Generating personalized result set by re-ranking
1 The documents are scored based on P(Q | D, θu)
2 Result set is re-arranged based on descending order of the
personalized score
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 33 / 71

34. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Experimental Dataset
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 34 / 71

35. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Parameter Tuning
Learning α and β Parameters
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 35 / 71

36. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Experimental Results & Analysis [1/2]
Evaluation on Real Dataset
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 36 / 71

37. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Experimental Results & Analysis [2/2]
Evaluation on AOL Dataset
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 37 / 71

38. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Motivation to Module 3
Summary of Module 2
1 Client side
personalization
2 Insensitive to the
number of Topics
3 Not all the queries
would require
personalization to be
performed
Explores topic model for finding
relevant documents using
topical features. To learn a
topic model on a representative
subset of a collection using
Genetic Intelligence technique
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 38 / 71

39. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
3. Genetic Intelligence Model for Document
Re-ranking
Problem Statement
How to represent documents as chromosomes?
How to evaluate fitness of search results?
Methodology
Apply GA with an adaptation of probabilistic model
Probabilistic similarity function has been used for fitness
evaluation
Documents are assigned a score based on the probability
of relevance
Probability of relevance are sought using GA approach in
order to optimize the search process i.e. finding of relevant
document not by assessing the entire corpus or collection
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 39 / 71

40. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Why GA for IR?
When the document search space represents a high
dimensional space i.e. the size of the document corpus is
multitude in IR
GA is the searching mechanisms known for its quick search
capabilities
When no relevant documents are retrieved in top order
with the initial query
The probabilistic exploration induced by GA allows the
exploration of new areas in the document space.
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 40 / 71

41. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Steps in GA for IR
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 41 / 71

42. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Fitness Evaluation
Representations of Chromosomes
Probabilistic Fitness Functions
1 P(q | d) = w∈d (P(q | w)P(w | d))
2 P(q | d) = αP(q | C) + (1 − α) w∈d (P(q | w)P(w | d))
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 42 / 71

43. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Selection & Reproduction
Roulette-wheel selection
Reproduction Operators
1 Crossover
2 Mutation
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 43 / 71

44. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Experimental Dataset
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 44 / 71

45. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Parameter Tuning
Learning Pc and Pm Parameters
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 45 / 71

46. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Experimental Results & Analysis [1/2]
Evaluation on Benchmark Dataset
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 46 / 71

47. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Experimental Results & Analysis [2/2]
Evaluation on Real Dataset
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 47 / 71

48. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Motivation to Module 4
Summary of Module 3
1 Explored the utility of
incorporating GA to
improve re-ranking
2 Adaptation of
personalization in GA
provides more desirable
results
3 Not all the queries
would require
personalization to be
performed
The graph representation of
documents best suit the
application of Swarm
Intelligence model. To simulate
ACO in graph structure based
on behavior of ants seeking a
path between their colony and
source of food for search query
reformulation
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 48 / 71

49. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
4. Swarm Intelligence Model for Search Query
Reformulation
Problem Statement
How to address vocabulary mismatch problem in IR?
How to change the original query to form a new query
that would find better relevant documents?
Methodology
Exploits Ant Colony Optimization (ACO) approach to
suggest related key words
The self-organizing principles which allow the highly
coordinated behavior of real ants that collaborate to solve
computational problems
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 49 / 71

50. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
ACO for Query Reformulation
Terminologies
Artificial Ant
Pheromone
Typical Ant System
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 50 / 71

51. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
ACO Implementation
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 51 / 71

52. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Characteristics of Artificial Ant
Notion of autocatalytic behavior
Chooses the query term to go with a transition probability
as a function of the similarity i.e. amount of trail present
on the connecting edge between terms
Navigation over retrieved documents for a query is treated
as ant movement over graph
When the user completes a tour, a substance called trail or
trace or pheromone is laid on each edge
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 52 / 71

53. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Similarity
Transition Probability
pk
ij (t) =
[τij (t)]α[ηij ]β
k[τik(t)]α[ηik]β
ηij is a static similarity weight
τij is a trace deposited by users
Trail Deposition
τij (t + 1) = p ∗ τij (t) + ∆τij
p is the rate of trail decay per time interval i.e. pheromone
evaporation factor
∆τij is the sum of deposited trails by users
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 53 / 71

54. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Experimental Dataset
AOL Search query log
Only the queries issued by at least 10 users were employed
and the pre-processed documents retrieved for that query
were used to construct graph
270 single and two word queries issued by different users
from AOL search log are taken
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 54 / 71

55. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Experimental Evaluation
Baseline Methods
Association 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
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 55 / 71

56. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Experimental Results & Analysis
Manual Evaluation
Benchmark Evaluation
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 56 / 71

57. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Summary
Summary of Module 4
1 Terms in the initial set of documents constitute potential
related terms
2 Semantically related keywords are suggested to the initial
query
3 Single word queries are treated as an ambiguous one
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 57 / 71

58. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Conclusion
1 Usage of Term Association Graph
Efficient retrieval of Journal articles
The graph structure may signify grammatical relations
between terms
2 Integration of Document Topic model and User Topic
model
Effective in general search
This may incorporate live user feedback
3 GA based document fitness evaluation
Good in document space exploration
Chromosomes representation may be improved
4 ACO based query reformulation
Exploits collaborative knowledge of users
If the solution is badly chosen, the probability of a bad
performance is high
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 58 / 71

59. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Further Extension
1 Efficient updating policy for user interest models
2 Account individual user specific context for generating
query refinements
3 Medical Information Retrieval (Eg. PubMed, WebMD,
etc.)
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 59 / 71

60. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Book References
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
Addison-Wesley .
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 60 / 71

61. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
References [1/7]
Matthijis & Radlinski (2012)
Personalizing Web Search using Long Term Browsing History
In Proc. 4th ACM WSDM , 25 – 34.
Agichtein et al (2006)
Improving Web Search Ranking by Incorporating user behavior
information
In Proc. 29th ACM SIGIR , 19 – 26.
Ponte & Croft (1998)
A language modeling approach to information retrieval
In Proc. 21st ACM SIGIR , 275 – 281.
Lafferty et al (2001)
Document language models, query models, and risk minimization for
information retrieval
In Proc. 24th ACM SIGIR , 111 – 119.
Kushchu (2005)
Web-Based Evolutionary and Adaptive Information Retrieval
IEEE Trans. Evolutionary Computation 9(2), 117 – 125.
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 61 / 71

62. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
References [2/7]
Leung & Lee (2010)
Deriving Concept-based User profiles from Search Engine Logs
IEEE Trans. Knowledge and Data Engineering 22(7), 969 – 982.
Blanco & Lioma (2012)
Graph-based term weighting for information retrieval
Springer Information Retrieval 15(1), 54 – 92.
Dorigo et al (2006)
Ant Colony Optimization
IEEE Computational Intelligence Magazine 1(4), 28 – 39.
Sugiyama et al (2004)
Adaptive web search based on user pro?le constructed without any
effort from users
In Proc. 13th Intl.Conf. World Wide Web, 675 – 684.
Brin Page (1998)
The Anatomy of a Large-Scale Hypertextual Web Search Engine
Elsevier Journal on Computer Networks and ISDN Systems 30(1-7),
107 – 117.
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 62 / 71

63. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
References [3/7]
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
In Proc. 2nd ACM Intl. Conf. Electronic commerce, 158 – 167.
Liu et al (2004)
Personalized web search for improving retrieval effectiveness
IEEE Trans. Knowledge and Data Engineering 16(1), 28 – 40.
Bennett et al (2012)
Modeling the impact of short- and long-term behavior on search
personalization
In Proc. 35th ACM SIGIR , 185 – 194.
cao et al (2008)
Context-Aware Query Suggestion by Mining Click-Through
In Proc. 14th ACM SIGKDD , 875 – 883.
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 63 / 71

64. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
References [4/7]
carman et al (2008)
Tag data and personalized Information Retrieval
In Proc. ACM workshop Search in social media , 27 – 34.
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
In Proc. 31st ACM SIGIR , 721 – 722.
white et al (2009)
Predicting user interests from contextual information
In Proc. 32nd ACM SIGIR , 363 – 370.
White et al (2013)
Enhancing personalized search by mining and modeling task behavior
In Proc. 22nd Intl. Conf. World Wide Web , 1411 – 1420.
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 64 / 71

65. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
References [5/7]
Vallet et al (2010)
Personalizing web search with Folksonomy-Based user and document
profiles
In Proc. 32nd European conference on Advances in IR, 420 – 431.
Jansen et al (2007)
Determining the user intent of web search engine queries
In Proc. 6th Intl. Conf. World Wide Web, 1149 – 1150.
Jansen et al (2000)
Real life, real users, and real needs: a study and analysis of user
queries on the web
Elsevier Information Processing and Management 36(2), 207 – 227.
Daoud et al (2008)
Learning user interests for a session-based personalized Search
In 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. World Wide Web , 666 – 674.VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 65 / 71

66. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
References [6/7]
Dang Croft (2010)
Query Reformulation Using Anchor Text
In Proc. 3rd ACM WSDM, 41 – 50.
Mei et al (2008)
Query suggestion using hitting time
In Proc. 17th ACM CIKM , 469 – 478.
Koren et al (2008)
Personalized interactive faceted search
In Proc. 17th Intl. Conf. World Wide Web, 477 – 486.
Wang Zhai (2005)
Mining term association patterns from search logs for effective query
Reformulation
In Proc.ACM CIKM , 479 – 488.
Huang Efthimiadis (2009)
Analyzing and evaluating query reformulation strategies in web search
logs
In Proc. 18th ACM CIKM , 77 – 86.
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 66 / 71

67. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
References [7/7]
Jain Mishne (2010)
Organizing query completions for web search
In Proc. ACM CIKM , 1169 – 1178.
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 information
retrieval
IEEE Trans. Knowledge and Data Engineering 24(12), 2260 – 2273.
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 67 / 71

68. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Journal Publications
Veningston, & Shanmugalakshmi (2015)
Semantic Association Ranking Schemes for Information Retrieval
Applications using Term Association Graph Representation
Sadhana - Academy Proceedings in Engineering Sciences , Springer
Publication. [Annexure I]
Veningston & Shanmugalakshmi (2014)
Computational Intelligence for Information Retrieval using Genetic
Algorithm
INFORMATION - An International Interdisciplinary Journal 17(8),
3825 – 3832. [Annexure I]
Veningston & Shanmugalakshmi (2014)
Combining User Interested Topic and Document Topic for
Personalized Information Retrieval
Lecture Notes in Computer Science Springer Publication 8883 , 60 –
79.[Annexure II]
Veningston & Shanmugalakshmi (2014)
Efficient Implementation of Web Search Query reformulation using
Ant Colony Optimization
Lecture Notes in Computer Science Springer Publication 8883 , 80 –VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 68 / 71

69. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
International Conference Publications [1/2]
Veningston, & Shanmugalakshmi (2015)
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 Term
Association 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 Web
Search
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 IEEE Xplore. [Best Paper]
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 69 / 71

70. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
International Conference Publications [2/2]
Veningston, Shanmugalakshmi & Ruksana (2013)
Context aware Personalization for Web Information Retrieval: A Large
scale probabilistic approach
In Proc. Intl. Conf. Cloud and Big Data Analytics , PSG College of
Technology.
Veningston & Shanmugalakshmi (2012)
Enhancing personalized web search Re-ranking algorithm by
incorporating 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

71. Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Association
Graph Model for
Document
Re-ranking
Topic Model for
Document
Re-ranking
Genetic
Intelligence
Model for
Document
Re-ranking
Swarm
Intelligence
Model for Search
Query
Reformulation
Conclusion
References
Publications
Thank You & Queries
VENINGSTON K (Dept. of CSE, GCT) Ph.D. Viva-Voce 05 August 2015 71 / 71