IR Theory: Web Information Retrieval

Web IR Fusion IR

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Evolution of IR: Phase I

Brute-force Search • User Raw Data

Library • Collection Development

Quality Control Classification Controlled Vocabulary Bibliographical Records

• Browsing User Organized/Filtered Data

• Searching User Intermediary Metadata Organized/Filtered Data

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Evolution of IR: Phase II

IR System • Automatic Indexing • Pattern Matching • User Computer Inverted Index Raw Data • Move from metadata to content-based search

IR Research • Goal

Rank the documents by their relevance to a given query • Approach

Query-Document Similarity Term-Weights based on term occurrence statistics

• Query Document Term Index Ranked list of matches • Controlled and restricted experiments

with small, homogeneous, and high quality data

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Evolution of IR: Phase III

World Wide Web • Massive, uncontrolled, heterogeneous, and dynamic environment • Content-based Web Search Engines

Web Crawler + Basic IR technology Matching of query terms to document terms

• Web Directories Browse/Search of Organized Web Manual cataloging of Web subset

• Content- & Link-based Web Search Engines Pattern Matching + Link Analysis

• Renewed interest in metadata and classification approach • Digital Libraries?

Integrated (Content, Link, Metadata) Information Discovery

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Fusion IR: Overview

Goal • To achieve the whole that is greater than sum of its parts

Approaches • Tag team

Use a method best suited for a given situation Single method, single set of results

• Integration Use a combined method that integrates multiple methods Combined method, single set of results

• Merging Merge the results of multiple methods Multiple methods, multiple sets of results

• Meta-fusion All of the above

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Fusion IR: Research Areas

Data Fusion • Combining multiple sources of evidence • Single collection, multiple representations, single IR method

Collection Fusion • Merging the results of multiple collection search • Multiple collections, single representation, single IR method

Method Fusion • Combining multiple IR methods • Single collection, single representation, multiple IR methods

Paradigm Fusion • Combining content analysis, link analysis, and classification • Integrating user, system, and data

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Fusion IR: Research Findings

Findings from content-based IR experiments with small, homogeneous document collections • Different IR systems retrieve

→ different sets of documents

• Documents retrieved by multiple systems → are more likely to be relevant

• Combining different systems → is likely to be more beneficial than combining similar systems

• Fusion is good for IR

Is fusion a viable approach for Web IR?

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Web Fusion IR: Motivation

Motivation

• Web search has become a daily information access mechanism

3.5 Billion Internet Users (350% growth from 2005) (internet live stats, 2016)

96% of Web users access the Internet daily. (www.internetsociety.org, 2012)

91% of Web users use search engines to find information. (Pew Internet, 2012)

3.5 billion Google searches per day (Internet Live Stats, 2016)

• New Challenges Data: massive, dynamic, heterogeneous, noisy Users: diverse, “transitory”

• New Opportunities Multiple sources of evidence

– content, hyperlinks, document structure, user data, taxonomies Data abundance/redundancy

Review • Yang (2005). Information Retrieval on the Web, ARIST Vol. 39 • http://kiyang.kmu.ac.kr/pubs/webir_arist.pdf

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Link Analysis: PageRank

PageRank score: R(pi) • Propagation of R(pi) through inlinks of the entire Web

T = total # of pages in the Web d = damping factor pi = inlink of p C(pi) = outdegree of pi

Start w/ all R(pi)=1, repeat computation until convergence

• Global Measure of a page based on link analysis only

• Interpretation Models the behavior of random Web surfer

– A probability distribution/weighting function that estimates the likelihood of arriving at page p by link traversal and random jump (d).

Importance/Quality/Popularity of a Web page – A link signifies recommendation/citation

– aggregate all recommendations recursively over entire Web, where each recommendation is weighted by its importance and normalized by its outdegree

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Link Analysis: HITS

Hyperlink Induced Topic Search • Consider both inlinks & outlinks

estimates the value of a page based on aggregate value of in/outlinks

• Identify “authority” & “hub” pages authority = a page pointed to by many good hubs

hub = a page pointing to many good authority

• Query-dependent measure hub & authority scores assigned for each query

computed from a small subset of the Web – i.e. top N retrieval results

• Premise Web contains mutually reinforcing communities of hubs & authorities on broad topics

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Link Analysis: Modified HITS

HITS-based Ranking

1. Expand a set of Text-based search results Root set S = top N documents (e.g. N=200) Inlinks & Outlinks of S (1 or 2 hops)

– Max. k inlinks per document (e.g. k=50) – Delete intrahost links, stoplist URLs

2. Compute Hub and Authority scores Iterative algorithm Fractional weights to links by same authors

3. Rank documents by Authority/Hub scores

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Modified HITS: Scoring Algorithm

1. Initialize all h(p) and a(p) to 1 2. Recompute h(p) and a(p) with fractional weights

- normalize contribution of authorship (assumption: host=author)

a(p)= ∑(h(q)*auth_wt(q,p)) q is a page linking to p auth_wt (q,p) = 1/m for page q, whose host has m documents linking to p

h(p)= ∑(a(q) *hub_wt(p,q)) q is a page linked from p hub_wt(p,q) = 1/n for page q, whose host has n documents linked from p

3. Normalize scores divide score by square root of sum of squared scores (∑a(p)= ∑h(p)=1)

4. Repeat steps 2 & 3 until scores stabilize

Typical convergence in 10 to 50 iterations for 5000 webpages

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Modified HITS: Link Weighting

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WIDIT:Web IR System Overview

1. Mine Multiple Sources of Evidence (MSE) Document Content Document Structure Link Information URL information

2. Execute Parallel Search Multiple Document Representations

body text, anchor text, header text Multiple Query formulations

query expansion

3. Combine the Parallel Search Results Static Tuning of fusion formula (QT-independent)

4. Identify Query Types (QT) Combination Classifier

5. Rerank the fusion result with MSE Compute Reranking Feature Scores Dynamic Tuning of reranking formulas (QT-specific)

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WIDIT: Web IR System Architecture

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Indexing Module

Sub-indexes

Body Index Anchor Index Header Index

Documents

Topics Queries

Simple Queries

Queries

Expanded Queries

Retrieval Module Fusion Module

Sub-indexes Sub-indexes

Search Results

Re-ranking Module

Fusion Result

Final Result

Static Tuning

Dynamic Tuning

Query Classification

Module

Query Types

WIDIT: Dynamic Tuning Interface

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SMART

• Length-Normalized Term Weights SMART lnu weight for document terms SMART ltc weight for query terms

where: fik = number of times term k appears in document i idfk = inverse document frequency of term k t = number of terms in document/query

• Document Score inner product of document and query vectors

where: qk = weight of term k in the query dik = weight of term k in document i

t = number of terms common to query & document

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• Document Ranking

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N = total number of documents in the collection n = total number of documents in which the term occur R = total number of relevant documents in the collection n = total number of relevant documents retrieved

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