Ranked Retrieval
LBSC 796/INFM 718R
Session 3
September 24, 2007
Agenda
•	Ranked retrieval

•	Similarity-based ranking

•	Probability-based ranking
The Perfect Query Paradox
•	Every information need has a perfect result set
–	All the relevant documents, no others

•	Every result set has a (nearly) perfect query
–	AND every word to get a query for document 1
•	Use AND NOT for every other known word
–	Repeat for each document in the result set
–	OR them to get a query that retrieves the result set
Boolean Retrieval
•	Strong points
–	Accurate, if you know the right strategies
–	Efficient for the computer
•	Weaknesses
–	Often results in too many documents, or none
–	Users must learn Boolean logic
–	Sometimes finds relationships that don’t exist
–	Words can have many meanings 
–	Choosing the right words is sometimes hard
Leveraging the User
Where Ranked Retrieval Fits
Ranked Retrieval Paradigm
•	Perform a fairly general search
–	One designed to retrieve more than is needed

•	Rank the documents in “best-first” order
–	Where best means “most likely to be relevant”

•	Display as a list of easily skimmed “surrogates” 
–	E.g., snippets of text that contain query terms
Advantages of Ranked Retrieval
•	Leverages human strengths, covers weaknesses
–	Formulating precise queries can be difficult
–	People are good at recognizing what they want

•	Moves decisions from query to selection time
–	Decide how far down the list to go as you read it

•	Best-first ranking is an understandable idea
Ranked Retrieval Challenges
•	“Best first” is easy to say but hard to do!
–	Computationally, we can only approximate it

•	Some details will be opaque to the user
–	Query reformulation requires more guesswork

•	More expensive than Boolean 
–	Storing evidence for “best” requires more space
–	Query processing time increases with query length
Simple Example:
Partial-Match Ranking
•	Form all possible result sets in this order:
–	AND all the terms to get the first set
–	AND all but the 1st term, all but the 2nd, …
–	AND all but the first two terms, …
–	And so on until every combination has been done
•	Remove duplicates from subsequent sets
•	Display the sets in the order they were made
–	Document rank within a set is arbitrary
Partial-Match Ranking Example
Agenda
•	Ranked retrieval

•	Similarity-based ranking

•	Probability-based ranking
What’s a Model?
•	A construct to help understand a complex system
–	A particular way of “looking at things”

•	Models inevitably make simplifying assumptions
Similarity-Based Queries
•	Model relevance as “similarity”
–	Rank documents by their similarity to the query
•	Treat the query as if it were a document
–	Create a query bag-of-words
•	Find its similarity to each document
•	Rank order the documents by similarity
•	Surprisingly, this works pretty well!
Similarity-Based Queries
•	Treat the query as if it were a document
–	Create a query bag-of-words
•	Find the similarity of each document
–	Using the coordination measure, for example
•	Rank order the documents by similarity
–	Most similar to the query first
•	Surprisingly, this works pretty well!
–	Especially for very short queries
Document Similarity
•	How similar are two documents?
–	In particular, how similar is their bag of words?
The Coordination Measure
•	Count the number of terms in common
–	Based on Boolean bag-of-words
•	Documents 2 and 3 share two common terms
–	But documents 1 and 2 share no terms at all
•	Useful for “more like this” queries
–	“more like doc 2” would rank doc 3 ahead of doc 1
•	Where have you seen this before?
Coordination Measure Example
Vector Space Model
Counting Terms
•	Terms tell us about documents
–	If “rabbit” appears a lot, it may be about rabbits
•	Documents tell us about terms
–	“the” is in every document -- not discriminating
•	Documents are most likely described well by rare terms that occur in them frequently
–	Higher “term frequency” is stronger evidence
–	Low “document frequency” makes it stronger still

McDonald's slims down spuds
Fast-food chain to reduce certain types of fat in its french fries with new cooking oil.
NEW YORK (CNN/Money) - McDonald's Corp. is cutting the amount of "bad" fat in its french fries nearly in half, the fast-food chain said Tuesday as it moves to make all its fried menu items healthier.
But does that mean the popular shoestring fries won't taste the same? The company says no. "It's a win-win for our customers because they are getting the same great french-fry taste along with an even healthier nutrition profile," said Mike Roberts, president of McDonald's USA.
But others are not so sure. McDonald's will not specifically discuss the kind of oil it plans to use, but at least one nutrition expert says playing with the formula could mean a different taste.
Shares of Oak Brook, Ill.-based McDonald's (MCD: down $0.54 to $23.22, Research, Estimates) were lower Tuesday afternoon. It was unclear Tuesday whether competitors Burger King and Wendy's International (WEN: down $0.80 to $34.91, Research, Estimates) would follow suit. Neither company could immediately be reached for comment.
…
16 × said 
14 × McDonalds
12 × fat
11 × fries
8 × new
6 × company, french, nutrition
5 × food, oil, percent, reduce,    
      taste, Tuesday
…

A Partial Solution: TF*IDF
•	High TF is evidence of meaning
•	Low DF is evidence of term importance
–	Equivalently high “IDF”
•	Multiply them to get a “term weight” 
•	Add up the weights for each query term
TF*IDF Example
The Document Length Effect
•	People want documents with useful parts
–	But scores are computed for the whole document

•	Document lengths vary in many collections
–	So frequency could yield inconsistent resutls

•	Two strategies
–	Adjust term frequencies for document length
–	Divide the documents into equal “passages”
Document Length Normalization
•	Long documents have an unfair advantage
–	They use a lot of terms
•	So they get more matches than short documents
–	And they use the same words repeatedly
•	So they have much higher term frequencies

•	Normalization seeks to remove these effects
–	Related somehow to maximum term frequency

“Cosine” Normalization
•	Compute the length of each document vector
–	Multiply each weight by itself
–	Add all the resulting values
–	Take the square root of that sum
•	Divide each weight by that length
Cosine Normalization Example
Formally …
Why Call It “Cosine”?
Interpreting the Cosine Measure
•	Think of query and the document as vectors
–	Query normalization does not change the ranking
–	Square root does not change the ranking
•	Similarity is the angle between two vectors
–	Small angle = very similar
–	Large angle = little similarity
•	Passes some key sanity checks
–	Depends on pattern of word use but not on length
–	Every document is most similar to itself
“Okapi BM-25” Term Weights
Passage Retrieval
•	Another approach to long-document problem
–	E.g., break it up into coherent units

•	Recognizing topic boundaries can be hard
–	Overlapping 300 word passages work well

•	Use best passage rank as the document’s rank
–	Passage ranking can also help focus examination
Summary
•	Goal: find documents most similar to the query

•	Compute normalized document term weights
–	Some combination of TF, DF, and Length

•	Sum the weights for each query term
–	In linear algebra, this is an “inner product” operation
Agenda
•	Ranked retrieval

•	Similarity-based ranking

•	Probability-based ranking
The Key Idea
•	We ask “is this document relevant?”
–	Vector space: we answer “somewhat”
–	Probabilistic: we answer “probably”

•	The key is to know what “probably” means
–	First, we’ll formalize that notion
–	Then we’ll apply it to ranking
“Noisy-Channel” Model of IR
Where do the probabilities fit?
Probabilistic Inference
•	Suppose there’s a horrible, but very rare disease

•	But there’s a very accurate test for it

•	Unfortunately, you tested positive…
Bayes’ Theorem
•	You want to find

•	But you only know
–	How rare the disease is
–	How accurate the test is
•	Use Bayes’ Theorem (hence Bayesian Inference)
Applying Bayes’ Theorem
Another View
Probability
•	Alternative definitions
–	Statistical: relative frequency as n  
–	Subjective: degree of belief

•	Thinking statistically
–	Imagine a finite amount of “stuff”
–	Associate the number 1 with the total amount
–	Distribute that “mass” over the possible events
Statistical Independence
•	A and B are independent if and only if:
    P(A and B) = P(A)  P(B)

•	Independence formalizes “unrelated”
–	P(“being brown eyed”) = 85/100
–	P(“being a doctor”) = 1/1000
–	P(“being a brown eyed doctor”) = 85/100,000

Dependent Events
•	Suppose”
–	P(“having a B.S. degree”) = 2/10
–	P(“being a doctor”) = 1/1000
•	Would you expect
–	P(“having a B.S. degree and being a doctor”)
	   = 2/10,000   ???
•	Extreme example:
–	P(“being a doctor”) = 1/1000
–	P(“having studied anatomy”) = 12/1000
Conditional Probability
•	P(A | B)    P(A and B) / P(B)
More on Conditional Probability
•	Suppose
–	P(“having studied anatomy”) = 12/1000
–	P(“being a doctor and having studied anatomy”) = 1/1000
•	Consider
–	P(“being a doctor” | “having studied anatomy”) = 1/12
•	But if you assume all doctors have studied anatomy
–	P(“having studied anatomy” | “being a doctor”) = 1
Some Notation
•	Consider 
–	A set of hypotheses: 	   H1, H2, H3
–	Some observable evidence	   O

•	P(O|H1) = probability of O being observed
			   if we knew H1 were true
•	P(O|H2) = probability of O being observed 
			   if we knew H2 were true
•	P(O|H3) = probability of O being observed
			   if we knew H3 were true
An Example
•	Let
–	O = “Joe earns more than $100,000/year”
–	H1 = “Joe is a doctor”
–	H2 = “Joe is a college professor”
–	H3 = “Joe works in food services”
•	Suppose we do a survey and we find out
–	P(O|H1) = 0.6
–	P(O|H2) = 0.07
–	P(O|H3) = 0.001
•	What should be our guess about Joe’s profession?
Bayes’ Rule
•	What’s P(H1|O)?  P(H2|O)?  P(H3|O)?
•	Theorem:

Back to the Example
•	Suppose we also have good data about priors:
–	P(O|H1) = 0.6	P(H1) = 0.0001  	doctor
–	P(O|H2) = 0.07	P(H2) = 0.001    	prof
–	P(O|H3) = 0.001	P(H3) = 0.2        	food
•	We can calculate
–	P(H1|O) =  0.00006	/ P(“earning > $100K/year”)
–	P(H2|O) =  0.0007	/ P(“earning > $100K/year”)
–	P(H3|O) =  0.0002	/ P(“earning > $100K/year”)
Key Ideas
•	Defining probability using frequency

•	Statistical independence

•	Conditional probability

•	Bayes’ rule

Probability Ranking Principle
•	Assume binary relevance, document independence
–	Each document is either relevant or it is not
–	Relevance of one doc reveals nothing about another

•	Assume the searcher works down a ranked list
–	Seeking some number of relevant documents

•	Theorem (provable from assumptions):
–	Documents should be ranked in order of decreasing probability of relevance to the query, 
			P(d relevant-to q)
Language Models
•	Probability distribution over strings of text
–	How likely is a string in a given “language”?

•	Probabilities depend on what language we’re modeling
Unigram Language Model
•	Assume each word is generated independently
–	Obviously, this is not true…
–	But it seems to work well in practice!
•	The probability of a string, given a model:
A Physical Metaphor
•	Colored balls are randomly drawn from an urn (with replacement)
An Example
Comparing Language Models
Retrieval w/ Language Models
•	Build a model for every document
•	Rank document d based on P(MD | q)
•	Expand using Bayes’ Theorem

•	Same as ranking by P(q | MD)
Visually …
Ranking Models?
Building Document Models
•	How do we build a language model for a document?
A First Try
•	Simply count the frequencies in the document = maximum likelihood estimate
Zero-Frequency Problem
•	Suppose some event is not in our observation S
–	Model will assign zero probability to that event
Why is this a bad idea?
•	Modeling a document
–	A word not appearing doesn’t mean it’ll never appear…
–	Safe to assume that unseen words are rare, though

•	Think of the document model as a topic
–	Many documents that can be written about a single topic
–	We try to guess the model is based on just one document

•	Practical effect: assigning zero probability to unseen words forces exact match
Smoothing
Implementing Smoothing
•	Assign some small probability to unseen events
–	But remember to take away “probability mass” from other events

•	Some techniques are easily understood
–	Add one to all the frequencies (including zero)

•	More sophisticated methods improve ranking
Recap: LM for IR
•	Indexing-time: 
–	Build a language model for every document
•	Query-time Ranking
–	Estimate the probability of generating the query according to each model
–	Rank the documents according to these probabilities
Language Model Advantages
•	Conceptually simple

•	Explanatory value

•	Exposes assumptions

•	Minimizes reliance on heuristics
Key Ideas
•	Probabilistic methods formalize assumptions
–	Binary relevance
–	Document independence
–	Term independence
–	Uniform priors
–	Top-down scan

•	Natural framework for combining evidence
–	e.g., non-uniform priors
A Critique
•	Most of the assumptions are not satisfied!
–	Searchers want utility, not relevance
–	Relevance is not binary
–	Terms are clearly not independent
–	Documents are often not independent

•	Smoothing techniques are somewhat ad hoc
But It Works!
•	Ranked retrieval paradigm is powerful
–	Well suited to human search strategies

•	Probability theory has explanatory power
–	At least we know where the weak spots are
–	Probabilities are good for combining evidence

•	Good implementations exist (e.g., Lemur)
–	Effective, efficient, and large-scale
Comparison With Vector Space
•	Similar in some ways
–	Term weights based on frequency
–	Terms often used as if they were independent

•	Different in others
–	Based on probability rather than similarity
–	Intuitions are probabilistic rather than geometric

A Complete System
•	Perform an initial Boolean query
–	Balancing breadth with understandability

•	Rerank the results
–	Using either Okapi or a language model
–	Possibly also accounting for proximity, links, …
One Minute Paper
•	Which assumption underlying the probabilistic retrieval model causes you the most concern, and why?