1. IR Theory: Web Information Retrieval

2. Fusion IR
Web IR
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3. 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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4. 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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5. 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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6. Fusion IR: Overview Goal Approaches
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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7. 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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8. 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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9. Web Fusion IR: Motivation
Web search has become a daily information access mechanism
79% of Web users access the Internet daily. 85% of Web users use search engines to find information.
GVU center at Georgia Tech (Kehoe et al., 1999)
670M Web searches per day (250M by Google)
Search Engine Watch (2/2003)
2 Billion Internet Users in 2010 (444% growth from 2000)
New Challenges
Data: massive, dynamic, heterogenious, 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
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10. 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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11. 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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12. Link Analysis: Modified HITS
HITS-based Ranking
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
Compute Hub and Authority scores
Iterative algorithm
Fractional weights to links by same authors
Rank documents by Authority/Hub scores
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13. Modified HITS: Scoring Algorithm
Initialize all h(p) and a(p) to 1
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
Normalize scores
divide score by square root of sum of squared scores (a(p)= h(p)=1)
Repeat steps 2 & 3 until scores stabilize
Typical convergence in 10 to 50 iterations for 5000 webpages
link weights
- normalize contribution of authorship
- divide each page contribution by #pages by the same author
- assumes site = author
Score normalization by length of score vector
- divide score by square root of sum of squared scores
- sum of scores add up to 1
Convergence
- max. 200 iterations
- Typical convergence in 10 to 50 iterations for 5000 Web pages
- Bharat & Henzinger: 150 iterations
Kleinberg, J. (1997). Authoritative sources in a hyperlinked environment. Proceeding of the 9th ACM-SIAM Symposium on Discrete Algorithms.
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14. Modified HITS: Link Weightingq3 q3 q4 q4
link weights
- normalize contribution of authorship
- divide each page contribution by #pages by the same author
- assumes site = author
Score normalization by length of score vector
- divide score by square root of sum of squared scores
- sum of scores add up to 1
Convergence
- max. 200 iterations
- Typical convergence in 10 to 50 iterations for 5000 Web pages
- Bharat & Henzinger: 150 iterations
Kleinberg, J. (1997). Authoritative sources in a hyperlinked environment. Proceeding of the 9th ACM-SIAM Symposium on Discrete Algorithms. p p q1 q2 q1 q2
a(p)= h(q1) + h(q2) + h(q3) + h(q4)/5
h(p)= a(q1) + a(q2) + a(q3) + a(q4)/6
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15. WIDIT:Web IR System Overview
Mine Multiple Sources of Evidence (MSE)
Document Content
Document Structure
Link Information
URL information
Execute Parallel Search
Multiple Document Representations
body text, anchor text, header text
Multiple Query formulations
query expansion
Combine the Parallel Search Results
Static Tuning of fusion formula (QT-independent)
Identify Query Types (QT)
Combination Classifier
Rerank the fusion result with MSE
Compute Reranking Feature Scores
Dynamic Tuning of reranking formulas (QT-specific)
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16. WIDIT: Web IR System Architecture
Body Index
Anchor Index
Header Index
Static Tuning
Sub-indexes
Sub-indexes
Sub-indexes
Documents
Indexing Module
Fusion Module
Retrieval Module
Search Results
Topics
Queries
Queries
Simple Queries
Expanded Queries
Fusion Result
Dynamic Tuning
Query Classification Module
Query Types
Re-ranking Module
Final Result
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17. WIDIT: Dynamic Tuning Interface
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18. SMART Length-Normalized Term Weights Document Score
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
Web documents have large variation in lengths
Length-normalized Term weights
- The denominator is a document­length normalization factor
- keeps short documents from being penalized.
Buckley, C., Salton, G., & Allan, J., & Singhal, A. (1995). Automatic query expansion using SMART: TREC 3. In D. K. Harman (Ed.), The Third Text Rerieval Conference (TREC-3) (NIST Spec. Publ , pp. 1-19). Washington, DC: U.S. Government Printing Office.
Buckley, C., Singhal, A., Mitra, M., & Salton, G. (1996). New retrieval approaches using SMART: TREC 4. In D. K. Harman (Ed.), The Fourth Text REtrieval Conference (TREC-4) (NIST Spec. Publ , pp ). Washington, DC: U.S. Government Printing Office.
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19. Okapi Document Ranking Document term weight (simplified formula)
where: Q = query containing terms T
K = k1 ((1-b) + b*(doc_length/avg.doc_length))
tf = term frequency in a document
qtf = term frequency in a query
k1 , b, k3 = parameters (1.2, 0.75, )
wRS = Robertson-Sparck Jones weight
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
Document term weight
(simplified formula)
Query term weight
Web documents have large variation in lengths
Length-normalized Term weights
- The denominator is a document­length normalization factor
- keeps short documents from being penalized.
Buckley, C., Salton, G., & Allan, J., & Singhal, A. (1995). Automatic query expansion using SMART: TREC 3. In D. K. Harman (Ed.), The Third Text Rerieval Conference (TREC-3) (NIST Spec. Publ , pp. 1-19). Washington, DC: U.S. Government Printing Office.
Buckley, C., Singhal, A., Mitra, M., & Salton, G. (1996). New retrieval approaches using SMART: TREC 4. In D. K. Harman (Ed.), The Fourth Text REtrieval Conference (TREC-4) (NIST Spec. Publ , pp ). Washington, DC: U.S. Government Printing Office.
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