Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Ph.D.
Viva-Voce
VENINGSTON
K
Objectives of
Research
Work
Introduction
Literature
Survey
Proposed
Research
Works
Term
Assoc...
Upcoming SlideShare
Loading in …5
×

Personalized Information Retrieval system using Computational Intelligence Techniques

2,114 views

Published on

Ph.D. Thesis Complete Presentation

Published in: Data & Analytics
  • Very nicely done.could you kindly share the latex code for this presentation to vanitharesearch@gmail.com
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • Be the first to like this

Personalized Information Retrieval system using Computational Intelligence Techniques

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

×