The document discusses personalization in information retrieval, extraction, and access. It describes how current search engines can be improved through deeper analysis of queries and content using natural language processing, information retrieval, and information extraction techniques. Personalization approaches are proposed, including using a user's search history and implicit feedback to learn profiles and improve future search results through re-ranking. Applications discussed include personalized search engines and summarization for mobile devices.

1. Personalization in Information Retrieval,
Extraction and Access
Workshop On Ontology, NLP, Personalization And IE/IR - IIT Bombay, Mumbai 15-17 July 2008
Vasudeva Varma
www.iiit.ac.in/~vasu

2. Search Engine Heat is On!
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Applications of Search Technologies
Web search
Product search
Service search
Domain Search
Already a BIG Market
HUGE Opportunity
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H 5/30/2008
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3. Agenda
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Evolution of Search Engines
Information Retrieval Vs. Extraction Vs. Access
Personalization in IR, IE and IA
Applications in Personalized IA
Conclusions
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H 5/30/2008
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4. Evolution of Search Engines
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Crawling and Indexing
Topic directories
Clustering and Classification
Hyperlink analysis
Resource discovery and vertical portals
Semantic Web
???
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H 5/30/2008
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5. Current IR engines fail – why?
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Wide variation in retrieval results
User topic
Retrieval system
Different approaches work for different systems.
No way to determine which approach will work for
a particular query.
Solution:
Deeper analysis of the content and Query
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H 5/30/2008
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6. Motivation for Deeper Analysis
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Texts are one of the major sources of
information and knowledge.
However, they are not transparent.
They have to be systematically integrated with
the other sources like data bases, numerical data,
etc.
NLP/IR/IE for better analysis
IA for better presentation
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H
5/30/2008
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7. Agenda
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Evolution of Search Engines
Information Retrieval Vs. Extraction Vs. Access
Personalization in IR, IE and IA
Applications in Personalized IA
Conclusions
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H 5/30/2008
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8. IR vs. IE vs. IA
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To search and retrieve documents in response to queries for
information
Vs.
To extract information that fits pre-defined database schemas or
templates, specifying the output formats
Vs.
To make the required information accessible to the user in their
choice of language, mode, level of detail and format
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H 5/30/2008
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9. Characterization of Texts
IR System
Queries
Collection of Texts
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10. Knowledge
Characterization of Texts Interpretation
IR System
Queries
Collection of Texts
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11. Knowledge
Characterization of Texts Interpretation
Passage
IR System Queries
Collection of Texts
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12. Knowledge
Characterization of Texts
Interpretation
Passage IE System
IR System Queries
Structures
of
Sentences
Collection of Texts NLP
Texts Templates
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13. Information Access
Knowledge
Technologies
Interpretation Machine
Translation
Passage IE System
IR System
Summarization
I
Snippet
Generation
NL Generation
Visualization
Tools
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14. Agenda
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Evolution of Search Engines
Information Retrieval Vs. Extraction Vs. Access
Personalization in IR, IE and IA
Applications in Personalized IA
Conclusions
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H 5/30/2008
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15. Limitations of Current IR Systems
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All users get same results for a given query –
independent of:
Previous search history
Current Search Context
Treat all users the same
Does one size fits all?
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H 5/30/2008
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16. Personalized Web Search
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Automatic adjustment of information content, structure, and
presentation tailored to an individual user.
Characteristics: Age, Gender, Special Interest Groups, Topic
Personalize Search Results using
Personal content
Past Activities (long term and short term)
Variations:
Explicit or Implicit profile setup
Explicit or Implicit relevance feedback
Client side or server side storage of information (privacy implications)
User control over amount of personalization
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H 5/30/2008
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17. Overview of Personalized Search
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Typically a 3 step process:
1. Obtain results (n>>10)
2. Computer Similarity (results, User)
3. Re-rank the results
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H 5/30/2008
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18. 18
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H 5/30/2008
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19. 19
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H 5/30/2008
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20. Techniques
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Co-active Techniques
Pro-active Techniques
Collaborative Filtering
User Profile based Result Pruning
User Profile based Query Expansion
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H 5/30/2008
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21. Problem Description
Personalized Search - Issues
What to use to Personalize?
How to Personalize?
When not to Personalize?
How to know Personalization helped?
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22. Problem Description
We focus on the issue How to Personalize?
Problem Statement
How to learn to personalize for future searches using
past search history
How to model and represent past search contexts
How to use it to improve search results
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23. Solution - Outline
Model and Represent past user feedback – Learning
user profile
Use implicit feedback
Long term learning
User contexts – triples
{user,query,{relevant documents}}
Improve Search Results – Reranking
Get Initial Search results
Take top few and rescore using user profile and rearrange
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24. Contributions
I Search : A suite of approaches for Personalized
Web Search
Proposed Personalized search approaches
Baseline
Basic Retrieval methods
Automatic Evaluation
Analysis of Query Log
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25. Review of Personalized Search
Personalized Search
Query logs Machine learning Language modeling Community based Others
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26. I Search : A suite of Techniques for
Personalized IR
Suite of Approaches???
Statistical Language modeling based approaches
Simple N-gram based methods
Noisy Channel Model based method
Machine learning based approach
Ranking SVM based method
Personalization without relevance feedback
Simple N-gram based method
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27. Statistical Language Modeling based
Approaches:Overview
From user contexts, capture statistical properties
of texts
Use the same to improve search results
Different Contexts
Unigram and Bigrams
Simple N-gram based approaches
Relationship between query and document words
Noisy Channel based approach
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28. Simple N-gram based approaches
N-gram : general term for words
1-gram : unigram, 2-gram : bigram
Capture statistical properties of text
Single words (Unigrams)
Two adjacent words (Bigrams)
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29. Learning user profile
Given Past search history
Hu = {(q1, rf1), (q2, rf2), …, (qn, rfn)}
rfall = contentation of all rf
For each unigram wi
User profile
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30. Sample user profile
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31. Reranking
In general LM for IR
Our Approach
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32. Noisy Channel based Approach
Documents and Queries different information spaces
Queries – short, concise
Documents – more descriptive
Most methods to retrieval or personalized web
search do not model this
Capture relationship between query and document
words
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33. Machine Learning based
Approaches:Introduction
Most machine learning for IR - Binary classification
problem – “relevant” and “non-relevant”
Click through data
Click is not an absolute relevance but relative relevance
i.e., assuming clicked – relevant, un clicked - irrelevant
is wrong.
Clicks – biased
Partial relative relevance - Clicked documents are more
relevant than the un clicked documents.
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34. Personalized Search without Relevance
Feedback:Introduction
Can personalized be done without relevance
feedback about which documents are relevant
How much informative are the queries posed by
users
Is information contained in the queries enough to
personalize?
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35. Approach
Past queries of the user available
Make effective use of past queries
Simple N-gram based approach
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36. Experiment Results
Language Modeling – Best Results!
Interesting framework Personalized Search
Simple N-gram based approaches also worked well
Noisy Channel model worked best
Extracting Synthetic Queries helped
Different Training schemes
IBM Model1 Vs GIZA++
Snippet Vs Document
Machine Learning – competitive results
Different Features and weights
Without Relevance Feedback – Very encouraging results
Simple Approach worked well
Sparsity – Query log was useful
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37. Agenda
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Evolution of Search Engines
Information Retrieval Vs. Extraction Vs. Access
Personalization in IR, IE and IA
Applications in Personalized IA
Conclusions
Personalized Search Personalized
Engine for Mobile Summarization
Phones (for Mobile Devices)
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H 5/30/2008
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38. “Personalized” Search Engine for
mobile devices
To develop a “personalized” Search Engine for mobile devices
that will produce more relevant results based on the query
and the “context”
What we mean by “Personalized” search?
user will be able to configure the search interfaces (Explicit feedback)
System will observe user behavior and customize itself to suit user’s
needs (Implicit feedback)
What we mean by “Context”?
User, time, location, …
Goal is to make Search accessible on Nokia mobile devices and make use of
the mobile aspects for personalization.
38 (C) Vasudeva Varma, IIIT Hyderabad, India
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39. Scope of the Application
Client Side Server Side
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40. Problem Re-Definition
Dynamic user behavior tracking
An observer that keeps track of all “relevant” user actions
Client module
Analysis of user actions
Interpret the user actions to derive user interests (categories of interests)
so that more relevant results are displayed
Construction of user profile implicitly
Implicit Supervised learning
Personalization
Based on Query
Based on User Profile
Based on other parameters such as time, location
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41. Solution Overview
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42. Personalized Summarization:
Motivation
The success that search engine providers have found on the PC
have failed to translate to the mobile phone. why?
Because trying to force a PC-based search experience inside a mobile
device falls short on a key area of usability
Search queries typically return hundreds of potential hits.
Making sense of such output is difficult.
The results may or may not be of user interest.
We are looking for a faster and easier way to access
precise information on our mobile devices.
42 (C) Vasudeva Varma, IIIT Hyderabad, India
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43. Challenges
Can we offer users a more simple, friendly and
intuitive experience?
We are looking forward to provide more
information with less payload in form of a summary
which will take care of
context
history
preferences
device capabilities
social network
43 (C) Vasudeva Varma, IIIT Hyderabad, India
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44. System Model
Search Engine
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(C) Vasudeva Varma, IIIT Hyderabad, India
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45. Summary
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Current Search Engines are inadequate and current
know-how is only the tip of an ice-berg
IR, IE and IA areas have enjoyed huge commercial
success and have a huge growth potential
Personalization is perhaps the next big wave
Various personalization techniques are available -
yet this is a very fertile research field
The two personalization application shown are just
examples of many possibilities.
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H 5/30/2008
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46. 46 Thank You – Questions?
Vasudeva Varma, IIIT Hyderabad
vv@iiit.ac.in or www.iiit.ac.in/~vasu
IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H 5/30/2008