CS276: Information Retrieval and Web Search
Pandu Nayak and Prabhakar Raghavan
Lecture 6: Scoring, Term Weighting and the Vector Space Model
Recap of lecture 5
 Collection and vocabulary statistics: Heaps’ and Zipf’s laws
 Dictionary compression for Boolean indexes
 Dictionary string, blocks, front coding
 Postings compression: Gap encoding, prefixunique codes
 VariableByte and Gamma codes














This lecture; IIR Sections 6.26.4.3
Ranked retrieval
Scoring documents
Term frequency
Collection statistics
Weighting schemes
Vector space scoring
Ranked retrieval
Thus far, our queries have all been Boolean.
Documents either match or don’t.
Good for expert users with precise understanding of their needs and the collection.
Also good for applications: Applications can easily consume 1000s of results.
Not good for the majority of users.
Most users incapable of writing Boolean queries (or they are, but they think it’s too much work).
Most users don’t want to wade through 1000s of results.
This is particularly true of web search.
Problem with Boolean search:
feast or famine
Boolean queries often result in either too few (=0) or too many (1000s) results.
Query 1: “standard user dlink 650” → 200,000 hits
Query 2: “standard user dlink 650 no card found”: 0 hits
It takes a lot of skill to come up with a query that produces a manageable number of hits.
AND gives too few; OR gives too many
Ranked retrieval models
Rather than a set of documents satisfying a query expression, in ranked retrieval, the system returns an ordering over the (top) documents in the collection for a query
Free text queries: Rather than a query language of operators and expressions, the user’s query is just one or more words in a human language
In principle, there are two separate choices here, but in practice, ranked retrieval has normally been associated with free text queries and vice versa
Feast or famine: not a problem in ranked retrieval
When a system produces a ranked result set, large result sets are not an issue
Indeed, the size of the result set is not an issue
We just show the top k ( ≈ 10) results
We don’t overwhelm the user
Premise: the ranking algorithm works
Scoring as the basis of ranked retrieval
We wish to return in order the documents most likely to be useful to the searcher
How can we rankorder the documents in the collection with respect to a query?
Assign a score – say in [0, 1] – to each document
This score measures how well document and query “match”.
Querydocument matching scores
We need a way of assigning a score to a query/document pair
Let’s start with a oneterm query
If the query term does not occur in the document: score should be 0
The more frequent the query term in the document, the higher the score (should be)
We will look at a number of alternatives for this.
Take 1: Jaccard coefficient
Recall from Lecture 3: A commonly used measure of overlap of two sets A and B
jaccard(A,B) = A ∩ B / A ∪ B
jaccard(A,A) = 1
jaccard(A,B) = 0 if A ∩ B = 0
A and B don’t have to be the same size.
Always assigns a number between 0 and 1.
Jaccard coefficient: Scoring example
What is the querydocument match score that the Jaccard coefficient computes for each of the two documents below?
Query: ides of march
Document 1: caesar died in march
Document 2: the long march
Issues with Jaccard for scoring
It doesn’t consider term frequency (how many times a term occurs in a document)
Rare terms in a collection are more informative than frequent terms. Jaccard doesn’t consider this information
We need a more sophisticated way of normalizing for length
Later in this lecture, we’ll use
. . . instead of A ∩ B/A ∪ B (Jaccard) for length normalization.
Recall (Lecture 1): Binary termdocument incidence matrix
Each document is represented by a binary vector ∈ {0,1}V
Termdocument count matrices
Consider the number of occurrences of a term in a document:
Each document is a count vector in ℕv: a column below
Bag of words model
Vector representation doesn’t consider the ordering of words in a document
John is quicker than Mary and Mary is quicker than John have the same vectors
This is called the bag of words model.
In a sense, this is a step back: The positional index was able to distinguish these two documents.
We will look at “recovering” positional information later in this course.
For now: bag of words model
Term frequency tf
The term frequency tft,d of term t in document d is defined as the number of times that t occurs in d.
We want to use tf when computing querydocument match scores. But how?
Raw term frequency is not what we want:
A document with 10 occurrences of the term is more relevant than a document with 1 occurrence of the term.
But not 10 times more relevant.
Relevance does not increase proportionally with term frequency.
NB: frequency = count in IR
Logfrequency weighting
The log frequency weight of term t in d is
0 → 0, 1 → 1, 2 → 1.3, 10 → 2, 1000 → 4, etc.
Score for a documentquery pair: sum over terms t in both q and d:
 The score is 0 if none of the query terms is present in the document.
score :
Document frequency
Rare terms are more informative than frequent terms
Recall stop words
Consider a term in the query that is rare in the collection (e.g., arachnocentric)
A document containing this term is very likely to be relevant to the query arachnocentric
→ We want a high weight for rare terms like arachnocentric.
Document frequency, continued
Frequent terms are less informative than rare terms
Consider a query term that is frequent in the collection (e.g., high, increase, line)
A document containing such a term is more likely to be relevant than a document that doesn’t
But it’s not a sure indicator of relevance.
→ For frequent terms, we want high positive weights for words like high, increase, and line
But lower weights than for rare terms.
We will use document frequency (df) to capture this.
idf weight
dft is the document frequency of t: the number of documents that contain t
dft is an inverse measure of the informativeness of t
df_{t} ≤ N
We define the idf (inverse document frequency) of t by
We use log (N/df_{t}) instead of N/df_{t} to “dampen” the effect of idf.
Will turn out the base of the log is immaterial.
idf example, suppose N = 1 million
There is one idf value for each term t in a collection.
idf_{t = }log_{10 }= ( N/df_{t})




 

 

 

 

 


Effect of idf on ranking
 Does idf have an effect on ranking for oneterm queries, like
iPhone
idf has no effect on ranking one term queries
idf affects the ranking of documents for queries with at least two terms
For the query capricious person, idf weighting makes occurrences of capricious count for much more in the final document ranking than occurrences of person.
Collection vs. Document frequency
The collection frequency of t is the number of occurrences of t in the collection, counting multiple occurrences.
Example:









 Which word is a better search term (and should get a higher weight)?
tfidf weighting
The tfidf weight of a term is the product of its tf weight and its idf weight.
W_{t,d} = log(1+ tf_{t,d}) x log_{10}(N/df_{t})
Best known weighting scheme in information retrieval
Note: the “” in tfidf is a hyphen, not a minus sign!
Alternative names: tf.idf, tf x idf
Increases with the number of occurrences within a document
Increases with the rarity of the term in the collection
Score for a document given a query
Score(q,d) = ∑_{t∈q∩d }tf.idf_{t,d}
There are many variants
How “tf” is computed (with/without logs)
Whether the terms in the query are also weighted
…
Binary → count → weight matrix
Each document is now represented by a realvalued vector of tfidf weights ∈ R^{V}
Documents as vectors
So we have a Vdimensional vector space
Terms are axes of the space
Documents are points or vectors in this space
Very highdimensional: tens of millions of dimensions when you apply this to a web search engine
These are very sparse vectors  most entries are zero.
Queries as vectors
 Key idea 1: Do the same for queries: represent them as vectors in the space
Key idea 2: Rank documents according to their proximity to the query in this space
proximity = similarity of vectors
proximity ≈ inverse of distance
Recall: We do this because we want to get away from the you’reeitherinorout Boolean model.
Instead: rank more relevant documents higher than less relevant documents
Formalizing vector space proximity
First cut: distance between two points
( = distance between the end points of the two vectors)
Euclidean distance?
Euclidean distance is a bad idea . . .
. . . because Euclidean distance is large for vectors of different lengths.
Why distance is a bad idea
The Euclidean distance between q and d_{2} is large even though the distribution of t terms in the query q and the distribution of terms in the document d_{2} are very similar.
Use angle instead of distance
Thought experiment: take a document d and append it to itself. Call this document d′.
“Semantically” d and d′ have the same content
The Euclidean distance between the two documents can be quite large
The angle between the two documents is 0, corresponding to maximal similarity.
Key idea: Rank documents according to angle with query.
From angles to cosines
The following two notions are equivalent.
Rank documents in decreasing order of the angle between query and document
Rank documents in increasing order of cosine(query,document)
Cosine is a monotonically decreasing function for the interval [0^{o}, 180^{o}]
From angles to cosines
But how – and why – should we be computing cosines?
Length normalization
A vector can be (length) normalized by dividing each of its components by its length – for this we use the L_{2} norm:
Dividing a vector by its L2 norm makes it a unit (length) vector (on surface of unit hypersphere)
Effect on the two documents d and d′ (d appended to itself) from earlier slide: they have identical vectors after lengthnormalization.
Long and short documents now have comparable weights
cosine(query,document)
qi is the tfidf weight of term i in the query
di is the tfidf weight of term i in the document
cos(q,d) is the cosine similarity of q and d … or,
equivalently, the cosine of the angle between q and d.
Cosine for lengthnormalized vectors
For lengthnormalized vectors, cosine similarity is simply the dot product (or scalar product):
for q, d lengthnormalized.
Cosine similarity illustrated
Cosine similarity amongst 3 documents
 How similar are the novels
 SaS: Sense and Sensibility
 PaP: Pride and Prejudice, and
 WH: Wuthering Heights?
 Term frequencies (counts)
 Note: To simplify this example, we don’t do idf weighting.




















3 documents example contd.
cos(SaS,PaP) ≈
0.789 × 0.832 + 0.515 × 0.555 + 0.335 × 0.0 + 0.0 × 0.0
≈ 0.94
cos(SaS,WH) ≈ 0.79
cos(PaP,WH) ≈ 0.69
Why do we have cos(SaS,PaP) > cos(SaS,WH)?
Computing cosine scores
tfidf weighting has many variants
Columns headed ‘n’ are acronyms for weight schemes.
Why is the base of the log in idf immaterial?
Weighting may differ in queries vs documents
Many search engines allow for different weightings for queries vs. documents
SMART Notation: denotes the combination in use in an engine, with the notation ddd.qqq, using the acronyms from the previous table
A very standard weighting scheme is: lnc.ltc
Document: logarithmic tf (l as first character), no idf and cosine normalization
Query: logarithmic tf (l in leftmost column), idf (t in second column), no normalization …
A bad idea?
tfidf example: lnc.ltc
Document: car insurance auto insurance
Query: best car insurance
Exercise: what is N, the number of docs?
Score = 0+0+0.27+0.53 = 0.8
Doc length =
Summary – vector space ranking
Represent the query as a weighted tfidf vector
Represent each document as a weighted tfidf vector
Compute the cosine similarity score for the query vector and each document vector
Rank documents with respect to the query by score
Return the top K (e.g., K = 10) to the user
Resources for today’s lecture
IIR 6.2 – 6.4.3
http://www.miislita.com/informationretrievaltutorial/cosinesimilaritytutorial.html
Term weighting and cosine similarity tutorial for SEO folk!