2007.03.20 - slide 1is 240 – spring 2007 prof. ray larson university of california, berkeley...
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2007.03.20 - SLIDE 1IS 240 – Spring 2007
Prof. Ray Larson University of California, Berkeley
School of InformationTuesday and Thursday 10:30 am - 12:00 pm
Spring 2007http://courses.ischool.berkeley.edu/i240/s07
Principles of Information Retrieval
Lecture 17: Latent Semantic Indexing
2007.03.20 - SLIDE 2IS 240 – Spring 2007
Overview
• Review– IR Components– Relevance Feedback
• Latent Semantic Indexing (LSI)
2007.03.20 - SLIDE 3IS 240 – Spring 2007
Relevance Feedback in an IR System
Interest profiles& Queries
Documents & data
Rules of the game =Rules for subject indexing +
Thesaurus (which consists of
Lead-InVocabulary
andIndexing
Language
StorageLine
Potentially Relevant
Documents
Comparison/Matching
Store1: Profiles/Search requests
Store2: Documentrepresentations
Indexing (Descriptive and
Subject)
Formulating query in terms of
descriptors
Storage of profiles
Storage of Documents
Information Storage and Retrieval System
Selected relevant docs
2007.03.20 - SLIDE 4IS 240 – Spring 2007
Query Modification
• Changing or Expanding a query can lead to better results
• Problem: how to reformulate the query?– Thesaurus expansion:
• Suggest terms similar to query terms
– Relevance feedback:• Suggest terms (and documents) similar to
retrieved documents that have been judged to be relevant
2007.03.20 - SLIDE 5IS 240 – Spring 2007
Relevance Feedback
• Main Idea:– Modify existing query based on relevance
judgements• Extract terms from relevant documents and add
them to the query• and/or re-weight the terms already in the query
– Two main approaches:• Automatic (psuedo-relevance feedback)• Users select relevant documents
– Users/system select terms from an automatically-generated list
2007.03.20 - SLIDE 6IS 240 – Spring 2007
Rocchio Method
0.25) to and 0.75 to set best to studies some(in terms
t nonrelevan andrelevant of importance thetune and ,
chosen documentsrelevant -non ofnumber the
chosen documentsrelevant ofnumber the
document relevant -non for the vector the
document relevant for the vector the
query initial for the vector the
2
1
0
121101
21
n
n
iS
iR
Q
where
Sn
Rn
i
i
i
n
i
n
ii
2007.03.20 - SLIDE 7IS 240 – Spring 2007
Rocchio/Vector Illustration
Retrieval
Information
0.5
1.0
0 0.5 1.0
D1
D2
Q0
Q’
Q”
Q0 = retrieval of information = (0.7,0.3)D1 = information science = (0.2,0.8)D2 = retrieval systems = (0.9,0.1)
Q’ = ½*Q0+ ½ * D1 = (0.45,0.55)Q” = ½*Q0+ ½ * D2 = (0.80,0.20)
2007.03.20 - SLIDE 8IS 240 – Spring 2007
Example Rocchio Calculation
)04.1,033.0,488.0,022.0,527.0,01.0,002.0,000875.0,011.0(
12
25.0
75.0
1
)950,.00.0,450,.00.0,500,.00.0,00.0,00.0,00.0(
)00.0,020,.00.0,025,.005,.00.0,020,.010,.030(.
)120,.100,.100,.025,.050,.002,.020,.009,.020(.
)120,.00.0,00.0,050,.025,.025,.00.0,00.0,030(.
121
1
2
1
new
new
Q
SRRQQ
Q
S
R
R
Relevantdocs
Non-rel doc
Original Query
Constants
Rocchio Calculation
Resulting feedback query
2007.03.20 - SLIDE 9IS 240 – Spring 2007
Rocchio Method
• Rocchio automatically– re-weights terms– adds in new terms (from relevant docs)
• have to be careful when using negative terms• Rocchio is not a machine learning algorithm
• Most methods perform similarly– results heavily dependent on test collection
• Machine learning methods are proving to work better than standard IR approaches like Rocchio
2007.03.20 - SLIDE 10IS 240 – Spring 2007
Probabilistic Relevance Feedback
Document Relevance
Documentindexing
Given a query term t
+ -
+ r n-r n
- R-r N-n-R+r N-n
R N-R N
Where N is the number of documents seenRobertson & Sparck Jones
2007.03.20 - SLIDE 11IS 240 – Spring 2007
Robertson-Spark Jones Weights
• Retrospective formulation --
rRnNrnrR
r
wnewt log
2007.03.20 - SLIDE 12IS 240 – Spring 2007
Robertson-Sparck Jones Weights
5.05.05.0
5.0
log)1(
rRnNrnrR
r
w
Predictive formulation
2007.03.20 - SLIDE 13IS 240 – Spring 2007
Using Relevance Feedback
• Known to improve results– in TREC-like conditions (no user involved)– So-called “Blind Relevance Feedback”
typically uses the Rocchio algorithm with the assumption that the top N documents in an initial retrieval are relevant
2007.03.20 - SLIDE 14IS 240 – Spring 2007
Blind Feedback
• Top 10 new terms taken from top 10 documents– Term selection is based on the classic
Robertson/Sparck Jones probabilistic model
2007.03.20 - SLIDE 15IS 240 – Spring 2007
Blind Feedback in Cheshire II
• Perform initial search (using TREC2 Probabilistic Algorithm) next slide
2007.03.20 - SLIDE 16IS 240 – Spring 2007
TREC2 Algorithm
logO(R | C,Q) co c1
1
Qc 1
qtf i
ql 35i1
Qc
c2
1
Qc 1log
tf i
cl 80i1
Qc
c3
1
Qc 1log
ctf i
N ti1
Qc
c4 Qc
|Qc| is the number of terms in common between the query and the componentqtf is the query term frequencyql is the query length (number of tokens)tfi is the term frequency in the component/documentcl is the number of terms in the component/documentctfi is the collection term frequency (number of occurrences in collection)Nt is the number of terms in the entire collection
2007.03.20 - SLIDE 17IS 240 – Spring 2007
Blind Feedback in Cheshire II
• Take top N documents and get the term vectors for those documents
• Calculate the Robertson/Sparck Jones weights for each term in the vectors– Note that collection
stats are used for non-rel documents (i.e. n, n-m, etc)
tt
tt
t
t
t
tttt
tttt
mmnn
mnmm
m
w
nmnm
nnmmnnmmnotindexed
nmnmindexed
nonrelrel
log
2007.03.20 - SLIDE 18IS 240 – Spring 2007
Blind Feedback in Cheshire II
• Rank the terms by wt and take the top M terms (ignoring those that occur in less than 3 of the top ranked docs)
• For the new query:– Use original freq weight * 0.5 as the weight for old
terms
– Add wt to the new query length for old terms
– Use 0.5 as the weight for new terms and add 0.5 to the query length for each term.
• Perform the TREC2 ranking again using the new query with the new weights and length
2007.03.20 - SLIDE 19IS 240 – Spring 2007
Koenemann and Belkin
• Test of user interaction in relevance feedback
2007.03.20 - SLIDE 20IS 240 – Spring 2007
Relevance Feedback Summary
• Iterative query modification can improve precision and recall for a standing query
• In at least one study, users were able to make good choices by seeing which terms were suggested for R.F. and selecting among them
• So … “more like this” can be useful!
2007.03.20 - SLIDE 21IS 240 – Spring 2007
Alternative Notions of Relevance Feedback
• Find people whose taste is “similar” to yours. Will you like what they like?
• Follow a users’ actions in the background. Can this be used to predict what the user will want to see next?
• Track what lots of people are doing. Does this implicitly indicate what they think is good and not good?
2007.03.20 - SLIDE 22IS 240 – Spring 2007
Alternative Notions of Relevance Feedback
• Several different criteria to consider:– Implicit vs. Explicit judgements – Individual vs. Group judgements– Standing vs. Dynamic topics– Similarity of the items being judged vs.
similarity of the judges themselves
2007.03.20 - SLIDE 23
Collaborative Filtering (social filtering)
• If Pam liked the paper, I’ll like the paper
• If you liked Star Wars, you’ll like Independence Day
• Rating based on ratings of similar people– Ignores the text, so works on text, sound,
pictures etc.– But: Initial users can bias ratings of future
users Sally Bob Chris Lynn KarenStar Wars 7 7 3 4 7Jurassic Park 6 4 7 4 4Terminator II 3 4 7 6 3Independence Day 7 7 2 2 ?
2007.03.20 - SLIDE 24
Ringo Collaborative Filtering (Shardanand & Maes 95)
• Users rate musical artists from like to dislike– 1 = detest 7 = can’t live without 4 = ambivalent– There is a normal distribution around 4– However, what matters are the extremes
• Nearest Neighbors Strategy: Find similar users and predicted (weighted) average of user ratings
• Pearson r algorithm: weight by degree of correlation between user U and user J– 1 means very similar, 0 means no correlation, -1 dissimilar– Works better to compare against the ambivalent rating (4), rather
than the individual’s average score
22 )()(
))((
JJUU
JJUUrUJ
2007.03.20 - SLIDE 25IS 240 – Spring 2007
Social Filtering
• Ignores the content, only looks at who judges things similarly
• Works well on data relating to “taste”– something that people are good at predicting
about each other too
• Does it work for topic? – GroupLens results suggest otherwise
(preliminary)– Perhaps for quality assessments– What about for assessing if a document is
about a topic?
2007.03.20 - SLIDE 26
Learning interface agents
• Add agents in the UI, delegate tasks to them• Use machine learning to improve performance
– learn user behavior, preferences
• Useful when:– 1) past behavior is a useful predictor of the future– 2) wide variety of behaviors amongst users
• Examples: – mail clerk: sort incoming messages in right mailboxes– calendar manager: automatically schedule meeting
times?
2007.03.20 - SLIDE 27IS 240 – Spring 2007
Example Systems
• Example Systems– Newsweeder– Letizia– WebWatcher– Syskill and Webert
• Vary according to– User states topic or not– User rates pages or not
2007.03.20 - SLIDE 28
NewsWeeder (Lang & Mitchell)
• A netnews-filtering system
• Allows the user to rate each article read from one to five
• Learns a user profile based on these ratings
• Use this profile to find unread news that interests the user.
2007.03.20 - SLIDE 29
Letizia (Lieberman 95)
• Recommends web pages during browsing based on user profile
• Learns user profile using simple heuristics • Passive observation, recommend on request• Provides relative ordering of link interestingness
• Assumes recommendations “near” current page are more valuable than others
user letizia
user profile
heuristics recommendations
2007.03.20 - SLIDE 30IS 240 – Spring 2007
Letizia (Lieberman 95)
• Infers user preferences from behavior• Interesting pages
– record in hot list– save as a file– follow several links from pages– returning several times to a document
• Not Interesting– spend a short time on document– return to previous document without following links– passing over a link to document (selecting links above
and below document)
2007.03.20 - SLIDE 31
WebWatcher (Freitag et al.)
• A "tour guide" agent for the WWW. – User tells it what kind of information is wanted– System tracks web actions– Highlights hyperlinks that it computes will be
of interest.
• Strategy for giving advice is learned from feedback from earlier tours. – Uses WINNOW as a learning algorithm
2007.03.20 - SLIDE 33
Syskill & Webert (Pazzani et al 96)
• User defines topic page for each topic• User rates pages (cold or hot) • Syskill & Webert creates profile with
Bayesian classifier– accurate– incremental– probabilities can be used for ranking of
documents– operates on same data structure as picking
informative features• Syskill & Webert rates unseen pages
2007.03.20 - SLIDE 35
Advantages
• Less work for user and application writer– compare w/ other agent approaches
• no user programming• significant a priori domain-specific and user
knowledge not required
• Adaptive behavior– agent learns user behavior, preferences over
time
• Model built gradually
2007.03.20 - SLIDE 36
Consequences of passiveness
• Weak heuristics– click through multiple uninteresting pages en
route to interestingness– user browses to uninteresting page, heads to
nefeli for a coffee– hierarchies tend to get more hits near root
• No ability to fine-tune profile or express interest without visiting “appropriate” pages
2007.03.20 - SLIDE 37
Open issues
• How far can passive observation get you?– for what types of applications is passiveness
sufficient?
• Profiles are maintained internally and used only by the application. some possibilities:– expose to the user (e.g. fine tune profile) ?– expose to other applications (e.g. reinforce belief)?– expose to other users/agents (e.g. collaborative
filtering)?– expose to web server (e.g. cnn.com custom news)?
• Personalization vs. closed applications• Others?
2007.03.20 - SLIDE 38IS 240 – Spring 2007
Relevance Feedback on Non-Textual Information
• Image Retrieval
• Time-series Patterns
2007.03.20 - SLIDE 42IS 240 – Spring 2007
Classifying R.F. Systems
• Standard Relevance Feedback– Individual, explicit, dynamic, item
comparison
• Standard Filtering (NewsWeeder)– Individual, explicit, standing profile, item
comparison
• Standard Routing– “Community” (gold standard), explicit,
standing profile, item comparison
2007.03.20 - SLIDE 43IS 240 – Spring 2007
Classifying R.F. Systems
• Letizia and WebWatcher– Individual, implicit, dynamic, item comparison
• Ringo and GroupLens:– Group, explicit, standing query, judge-based
comparison
2007.03.20 - SLIDE 44IS 240 – Spring 2007
Classifying R.F. Systems
• Syskill & Webert:– Individual, explicit, dynamic + standing, item
comparison
• Alexa: (?)– Community, implicit, standing, item
comparison, similar items
• Amazon (?):– Community, implicit, standing, judges + items,
similar items
2007.03.20 - SLIDE 45IS 240 – Spring 2007
Summary
• Relevance feedback is an effective means for user-directed query modification.
• Modification can be done with either direct or indirect user input
• Modification can be done based on an individual’s or a group’s past input.
2007.03.20 - SLIDE 47IS 240 – Spring 2007
LSI Rationale
• The words that searchers use to describe the their information needs are often not the same words used by authors to describe the same information.
• I.e., index terms and user search terms often do NOT match– Synonymy– Polysemy
• Following examples from Deerwester, et al. Indexing by Latent Semantic Analysis. JASIS 41(6), pp. 391-407, 1990
2007.03.20 - SLIDE 48IS 240 – Spring 2007
LSI Rationale
Access Document Retrieval Information Theory Database Indexing Computer REL MD1 x x x x x RD2 x* x x* MD3 x x* x * R M
Query: IDF in computer-based information lookup
Only matching words are “information” and “computer”D1 is relevant, but has no words in the query…
2007.03.20 - SLIDE 49IS 240 – Spring 2007
LSI Rationale
• Problems of synonyms– If not specified by the user, will miss
synonymous terms– Is automatic expansion from a thesaurus
useful?– Are the semantics of the terms taken into
account?
• Is there an underlying semantic model of terms and their usage in the database?
2007.03.20 - SLIDE 50IS 240 – Spring 2007
LSI Rationale
• Statistical techniques such as Factor Analysis have been developed to derive underlying meanings/models from larger collections of observed data
• A notion of semantic similarity between terms and documents is central for modelling the patterns of term usage across documents
• Researchers began looking at these methods that focus on the proximity of items within a space (as in the vector model)
2007.03.20 - SLIDE 51IS 240 – Spring 2007
LSI Rationale
• Researchers (Deerwester, Dumais, Furnas, Landauer and Harshman) considered models using the following criteria– Adjustable representational richness– Explicit representation of both terms and
documents– Computational tractability for large databases
2007.03.20 - SLIDE 52IS 240 – Spring 2007
LSI Rationale
• The only method that satisfied all three criteria was Two-Mode Factor Analysis– This is a generalization of factor analysis based on
Singular Value Decomposition (SVD)– Represents both terms and documents as vectors in a
space of “choosable dimensionality”– Dot product or cosine between points in the space
gives their similarity– An available program could fit the model in O(N2*k3)
2007.03.20 - SLIDE 53IS 240 – Spring 2007
How LSI Works
• Start with a matrix of terms by documents• Analyze the matrix using SVD to derive a
particular “latent semantic structure model”• Two-Mode factor analysis, unlike
conventional factor analysis, permits an arbitrary rectangular matrix with different entities on the rows and columns – Such as Terms and Documents
2007.03.20 - SLIDE 54IS 240 – Spring 2007
How LSI Works
• The rectangular matrix is decomposed into three other matices of a special form by SVD– The resulting matrices contain “singular
vectors” and “singular values”– The matrices show a breakdown of the original
relationships into linearly independent components or factors
– Many of these components are very small and can be ignored – leading to an approximate model that contains many fewer dimensions
2007.03.20 - SLIDE 55IS 240 – Spring 2007
How LSI Works
• In the reduced model all of the term-term, document-document and term-document similiarities are now approximated by values on the smaller number of dimensions
• The result can still be represented geometrically by a spatial configuration in which the dot product or cosine between vectors representing two objects corresponds to their estimated similarity
• Typically the original term-document matrix is approximated using 50-100 factors
2007.03.20 - SLIDE 56IS 240 – Spring 2007
How LSI Works
TitlesC1: Human machine interface for LAB ABC computer applicationsC2: A survey of user opinion of computer system response timeC3: The EPS user interface management systemC4: System and human system engineering testing of EPSC5: Relation of user-percieved response time to error measurementM1: The generation of random, binary, unordered treesM2: the intersection graph of paths in treesM3: Graph minors IV: Widths of trees and well-quasi-orderingM4: Graph minors: A survey
Italicized words occur and multiple docs and are indexed
2007.03.20 - SLIDE 57IS 240 – Spring 2007
How LSI Works
Terms Documents c1 c2 c3 c4 c5 m1 m2 m3 m4Human 1 0 0 1 0 0 0 0 0Interface 1 0 1 0 0 0 0 0 0Computer 1 1 0 0 0 0 0 0 0User 0 1 1 0 1 0 0 0 0System 0 1 1 2 0 0 0 0 0Response 0 1 0 0 1 0 0 0 0Time 0 1 0 0 1 0 0 0 0EPS 0 0 1 1 0 0 0 0 0Survey 0 1 0 0 0 0 0 0 0Trees 0 0 0 0 0 1 1 1 0Graph 0 0 0 0 0 0 1 1 1Minors 0 0 0 0 0 0 0 1 1
2007.03.20 - SLIDE 58IS 240 – Spring 2007
How LSI Works
Dimension 2
Dimension 1
11graphM2(10,11,12)
10 Tree12 minor
9 survey
M1(10) 7 time
3 computer
4 user6 response
5 system
2 interface1 human
M4(9,11,12)
M2(10,11)C2(3,4,5,6,7,9)
C5(4,6,7)
C1(1,2,3)
C3(2,4,5,8)
C4(1,5,8)
Q(1,3)Blue dots are termsDocuments are red squaresBlue square is a queryDotted cone is cosine .9 from Query “Human Computer Interaction”-- even docs with no terms in common(c3 and c5) lie within cone.
SVD to 2 dimensions
2007.03.20 - SLIDE 59IS 240 – Spring 2007
How LSI Works
X T0=
S0 D0’
txd txm mxm mxd
X = T0S0D0’
docs
terms
T0 has orthogonal, unit-length columns (T0’ T0 = 1)D0 has orthogonal, unit-length columns (D0’ D0 = 1)S0 is the diagonal matrix of singular valuest is the number of rows in Xd is the number of columns in Xm is the rank of X (<= min(t,d)
2007.03.20 - SLIDE 60IS 240 – Spring 2007
How LSI Works
X = TSD’T has orthogonal, unit-length columns (T’T = 1)D has orthogonal, unit-length columns (D’ D = 1)S0 is the diagonal matrix of singular valuest is the number of rows in Xd is the number of columns in Xm is the rank of X (<= min(t,d)k is the chosen number of dimensions in the reduced model (k <= m)
X T=
S D’
txd txk mxk kxd
docs
terms^
^
2007.03.20 - SLIDE 61IS 240 – Spring 2007
Comparisons in LSI
• Comparing two terms
• Comparing two documents
• Comparing a term and a document
2007.03.20 - SLIDE 62IS 240 – Spring 2007
Comparisons in LSI
• In the original matrix these amount to:– Comparing two rows– Comparing two columns– Examining a single cell in the table
2007.03.20 - SLIDE 63IS 240 – Spring 2007
Comparing Two Terms
• Dot product between the row vectors of X(hat) reflects the extent to which two terms have a similar pattern of occurrence across the set of documents
2007.03.20 - SLIDE 64IS 240 – Spring 2007
Comparing Two Documents
• The dot product between two column vectors of the matrix X(hat) which tells the extent to which two documents have a similar profile of terms
2007.03.20 - SLIDE 65IS 240 – Spring 2007
Comparing a term and a document
• Treat the query as a pseudo-document and calculate the cosine between the pseudo-document and the other documents
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