seminar topic

Personalization in Information Retrieval, Extraction and AccessWorkshop On Ontology, NLP, Personalization And IE/IR - IIT Bombay, Mumbai 15-17 July 2008

Vasudeva Varma

www.iiit.ac.in/~vasu

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Search Engine Heat is On!

IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H

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� Applications of Search Technologies

�Web search

�Product search

�Service search

�Domain Search

� Already a BIG Market

� HUGE Opportunity

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Agenda

5/30/2008IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H

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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

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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

� ???

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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

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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 presentation5/30/2008

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Agenda


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� Conclusions

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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 theirchoice of language, mode, level of detail and format

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IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H9

Collection of Texts

IR System

Characterization of Texts

Queries

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Collection of Texts

IR System


Queries

Interpretation

Knowledge

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Collection of Texts

Passage

IR System


Queries

Interpretation

Knowledge

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Collection of Texts

Passage

IR System


Queries

Interpretation

Knowledge

IE System

Texts Templates

Structures

of

Sentences

NLP

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I


Passage

IR System

Interpretation

Knowledge

IE System

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Machine

Translation

Summarization

Visualization

Tools

Information Access

Technologies

Snippet

Generation

NL Generation

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Agenda


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� Conclusions

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Limitations of Current IR Systems15

� 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?


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Personalized Web Search16

� 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


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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

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19 5/30/2008IR and IE Technologies and Personalization (C) Vasudeva Varma IIIT-H

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Techniques


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� Co-active Techniques

� Pro-active Techniques

� Collaborative Filtering

� User Profile based Result Pruning

� User Profile based Query Expansion

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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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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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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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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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Review of Personalized Search

Personalized Search

Query logs Machine learning Language modeling Community based Others

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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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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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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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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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Sample user profile

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Reranking

� In general LM for IR

� Our Approach

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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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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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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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Approach

� Past queries of the user available

� Make effective use of past queries

� Simple N-gram based approach

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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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Agenda


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� Conclusions

Personalized Search Engine for Mobile Phones

Personalized Summarization (for Mobile Devices)

38 (C) Vasudeva Varma, IIIT Hyderabad, India38

“Personalized” Search Engine for mobile devices

� To develop a “personalized” Search Engine for mobile devices that will produce more relevant results based on the queryand 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.


Scope of the Application

Client Side Server Side


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


Solution Overview


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.


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

44 (C) Vasudeva Varma, IIIT Hyderabad, India

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System Model

Search Engine

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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.

Vasudeva Varma, IIIT Hyderabad

[email protected] or www.iiit.ac.in/~vasu

Thank You – Questions?

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