web search result diversification

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Web Search Result

Diversification

Farhan Ahmad

Apr 2011



Original Paper

Diversifying Search Results

Rakesh AgrawalSearch Labs

Microsoft Research

Sreenivas GollapudiSearch Labs

Microsoft Research

Alan HalversonSearch Labs

Microsoft Research

Samuel IeongSearch Labs

Microsoft Research

Second ACM International Conference on Web Search and Data MiningWSDM 2009

Barcelona, Spain - February 9-12, 2009



Abstract

Query terms given by users are oftenambiguous.



Abstract


Search engines should diversify thesearch results to minimize the risk of dissatisfaction of average users.



Abstract



The authors have presented a systematicapproach for measuring diversity of search

results.



Abstract



The authors have presented a systematicapproach for measuring diversity of search

results. They have presented an algorithm to

maximize the diversity of a subset of thesearch results.



Introduction: Ambiguousqueries

Consider the search term 'FLASH'




Consider the search term 'FLASH'

It can have several interpretations-

− Flash player − Flash floods

− Flash Gordon (an adventure hero)




Suppose Flash player is searched mostoften.

Most of the top results returned for thequery 'FLASH' will belong to this category.

This is because search engines rank onthe basis of similarity, and make no explicit

attempt to diversify the documents.



Introduction:Relevance of search results

The basic premise is “ The relevance of aset of documents depends not only on theindividual relevance of its members, but

also on how they relate to each other.”Jaime G. Carbonell and Jade Goldstein. The use of MMR, diversity-based re-ranking for

reordering documents and producing summaries.

Ideally the result set should properlyaccount for the interests of the overall user population.



Introduction: BasicAssumption #1

A taxonomy of information exists at thetopical level.

A document can belong to one or more

categories, and so can a query.



Introduction: BasicAssumption #2

Usage statistics are available for user intents.

Example: When searching for 'FLASH',

65% users intended to find 'Flash Player',15% were looking for 'Recent flash floods'and 5% were looking for 'Flash Gordon'.



Introduction: Defining theobjective

To maximize the relevance of a resultdocument set based on individualrelevance of the members and their

diversity.



Formalization of Notation

The set of categories to which a documentd belongs is denoted by C(d).

The set of categories to which a querybelongs is denoted by C(q).




Example:

− C(q='FLASH') = { 'flash floods','Flashplayer','Flash Gordon') }

− C(d='FLASH') = { 'flash floods','Flashplayer', 'Flash Gordon', 'Flash village' }




C(d) ∩C(q) may be empty.




The probability of a given query qbelonging to a category c is denoted byP(c|q).

It is called user intent for query q andcategory c.




Assumption: Our knowledge is complete.cƐC(q), Ʃ P(c|q) = 1

Informally this means that given a queryq, we have an exhaustive list of all thecategories to which the query couldbelong.




V(d|q,c) is defined as the relevance valueof document d for query q, when theintended category of q is c.




V(d|q,c) is defined as the relevance valueof document d for query q, when theintended category is c.

If we constrain V to [0,1], it represents the

probability of document d satisfying user query q that has intended category c.




V can be obtained by multiplying query-document similarity by the probability thatthe document d belongs to category c.

− V(d|q,c) = Similarity(d,q) * P(c|d)

− Where P(c|d) can be computed by theclassifier algorithm . E.g. a confidencevalue.




Assumption: Given a query q and acategory of intent c, the relevance of twodocuments is independent

− V(d1|q,c) , V(d

2|q,c) are independent.



Formalizing the objective fn

Suppose users only consider the top kresults of a search engine.

– We can rephrase the objective :

–

As:“To maximize the relevance of a result document

set based on individual relevance of themembers and their diversity”.

The objective is to maximize theprobability that an average user finds at-least one relevant resultamong the top k.




Formally, given− A query q,

− A set of documents D

−

A distribution of category of intent P(c|q),− The relevance of each document d ε D

V(d|q,c),

− We want to find the set S of top k results

(|S| = k), S⊆ D ,such that




P(S|q)= ∑

cP(c|q) . (1- π

d S∈(1-V(d|q,c) ) )

is maximized



Origin of the objective function

d S∈ V(d|q,c) is the probability that adocument d from our result subset Ssatisfies a user query q having intendedcategory c.




d S∈ 1-V(d|q,c) is the probability that adocument d, from our result subset S,does not satisfy a user query q, havingintended category c.




πd S∈ (1-V(d|q,c) ) is the probability that nodocument d, from our result subset S,satisfies user query q, having intendedcategory c.




1- πd S∈ (1-V(d|q,c) ) is the probabilitythat at least one document d, from our result subset S, satisfies user query q,having intended category c.




Therefore,P(c|q) . (1- π

d S∈(1-V(d|q,c) ) )

gives the probability that query q has

intended category c, and it is satisfied byat least one document from our resultsubset S.




If we sum upP(c|q) . (1- π

d S∈(1-V(d|q,c) ) )

for different categories {c1,c

2,...,c

r },

we find the probability that a user querybelonging to any of these categories issatisfied by at least one document from

our result subset S.




Therefore by definingP(S|q) = ∑

cP(c|q) . (1- π

d S∈(1-V(d|q,c) ) ),

and trying to maximize P(S|q), we are trying

to maximize the chances that an averageuser is satisfied.

That is P(S|q) is measuring the diversity of result subset S for a query q.



Caveat

Diversify(k) does not try to cover all thecategories

− While trying to maximize

∑c P(c|q) . (1- πd S∈ (1-V(d|q,c) ) ),− And maintain |S| = k

We might need to exclude all documents

from some category cr .



Caveats

It might be that by taking all k documentsfrom only a single category c3,

we are able

to maximize P(S|q) !!

All other categories are left out in such acase.



Problems with Diversify(k)

Diversify(k) does not say anything aboutthe ordering of the result subset S.

Diversify(k) is NP hard (it reduces to theproblem of finding the max coverage of

result set D).

A greedy algorithm for



A greedy algorithm for Diversify(k)

The authors have proposed a greedyalgorithm for Diversify(k) that uses theconcept of marginal utility to diversify aswell as re-rank the search results.

This algorithm maximizes P(S|q) whenevery document can belong to just onecategory, and otherwise it optimizes P(S|q)with bounded error.



Notation

U(c|q,S) is the probability that a query qbelongs to the category c, given that alldocuments in the set S fail to satisfy theuser.

− Initially S= ∅

− And we define U(c|q, ) = P(c|q)∅



Notation

We define the marginal utility of adocument d as the product of its relevancevalue V with the conditional distribution of categories U

g(d|q,c,S) = ∑c C(d)∈ U(c|q,S) . V(d|q,c)

− It is the probability that document dsatisfies the user when all documentsthat come before it fail to do so.



Greedy algo IA-Select10: D = D – {d*}

11:ENDWHILE

12:RETURN S

P f



Proofs

•

Earlier we claimed that – IA-select maximizes P(S|q), the diversity if

every document belongs to exactly onecategory.

– IA-select optimizes P(S|q), with boundederror, otherwise.

B i f f



Basis for proofs

•

To prove our claims, we need tounderstand the concept of submodularity.

• We first define submodularity.

• Then prove that P(S|q) is submodular

• Then prove our claims.

S b d l it



Submodularity

•

It is known as the principle of diminishingmarginal utilities in economics.

• In our context, the marginal benefit of adding a document to a larger

collection is less than that of adding thesame document to a smaller collection.

• Formal definition follows.

S b d l it



Submodularity

•

If N is a set and f is a set functionf: 2N ==> R

then f is submodular if and only if

for S T N⊆ ⊆and d N and d S , d T∊ ∉ ∉

f(S U {d}) – f(S) f(T U {d}) - f(T)≧

Submodularity



Submodularity

•

We have chosen two subsets of thedomain N, S and T. S is smaller than Twhich in turn is smaller than N.

• Then for a new element d in N

which has not yet been added to either S or T.

Submodularity



Submodularity

We evaluate the change in values of f due toaddition of d, for S and T both.

• f(S U {d}) – f(S) is the marginal utilitygained from adding d to the smaller set

S.• f(T U {d}) – f(T) is the marginal utility

gained from adding d to the larger set T.

Submodularity



Submodularity

•

If the inequalityf(S U {d}) – f(S) f(T U {d}) – f(T) holds,≧

f is said to be submodular.

P(S|q) is submodular




•

Let S,TS T D⊆ ⊆

be two sets of documents.

• And e D be a document such that∊e T∉

• Let S' = S {e} and T' = T {e}⋃ ⋃





• P(S'|q) – P(S|q)

= P(c|q) . [ (1- πd S'∈

(1-V(d|q,c) ) ) - (1-

πd S∈

(1-V(d|q,c) ) ) ]

= P(c|q) . [ πd S∈

(1-V(d|q,c) - πd S'∈

(1-V(d|

q,c) )]





• Similarly

• P(T'|q) – P(T|q)

P(c|q) . (πd T∈

(1-V(d|q,c) ) ) . V(e|q,c)





• Therefore

P(S'|q) – P(S|q) P(T'|q) – P(T|q)≧

• Hence P(S|q) is submodular.

Proof #1



Proof #1

• Formally, we have to prove that

- IA-select maximizes P(S|q) if

∀d D, |C(d)|=1∈

Proof #1



Proof #1

• Because U(c|q,S) = P(c|q) at the outset,

as documents { d1, d

2,..} get added to S,

in step 8 U is updated as

– c=C(d1),

U(c|q,S) = (1 – V(d1|q,c) ) . U(c|q, )∅

= (1 – V(d1|q,c) ) . P(c|q)

– c=C(d2),

U(c|q,S) = (1 – V(d2|q,c) ) . (1 – V(d

1|q,c) ) . U(c|q, )∅

= (1 – V(d2|q,c) ) .(1 – V(d

1|q,c) ) . P(c|q)

Proof #1



Proof #1

• From the result for submodularity of P(S|q) we know that

for every document d

∑c

g(d|q,c,S) = P( S {d} | q) – P(S|q)∪

– Because g(d|q,c,S) is non-zero for exactly one category c, the sigma

can be removed

g(d|q,c,S) = P( S {d} | q) – P(S|q)∪

Proof #1



Proof #1

• At the start S = . When we have added∅

required k documents to S

g(d1|q,c, )∅ = P( {d

1} | q) – P( |q)∅

g(d2|q,c,{d

1}) = P( {d

2,d

1} | q ) - P({d

1} | q)

. . .

. . .

. . .

g(dk|q,c,{d1,d2...dk-1} ) = P(S | q) – P({d1,d2...dk-1} | q)

Proof #1



• The sum is

g(d1|q,c, ) + g(d∅

2|q,c,{d

1}) + ......... + g(d

k|q,c,{d

1,d

2...d

k-1} )

= P(S|q) – P( |q)∅

=P(S|q)



Proof #2



• Formally we have to prove that if documents can belong to manycategories, their selection based on g(d|q,c,S) still optimizes P(S|q), with an error that is bounded.



Proof #2



• Since our objective P(S|q) is submodular,and our algorithm IA-Select() is greedy inthe sense mentioned before,

– IA-Select is a (1-1/e) approximation to

Diversify(k). – That is, IA-select optimizes P(S|q) and the

optimum value is not less than (1-1/e) themaximum value obtainable fromDiversify(K).

Evaluating IA-Select



g

• To measure the result set diversity of search engines and compare it with thediversity of results obtained using IA-Select, the authors first defined intent-aware counterparts of traditional IRmetrics.

Evaluating IA select



• One such metric is reciprocal rank(RR) :It is the inverse of the first position atwhich a relevant document is found in alist.

• If there is a rank-threshold T, RR is zeroif no relevant document is found amongthe first T documents.

• The mean reciprocal rank (MRR) of aquery set is the average RR of thequeries in the set.




• Because IA-Select diversifies the top kresults, the authors defined an IA MRR

• For a result set D, rank threshold k

and query q

– MRR-IA(D,k) = ∑c

P(c|q) . MRR(D,k|c)

MRR(D,k|c) gives the average RR for aquery set belonging to category c.




• Shown below are the results obtainedusing three commercials search engineand IA-Select.

web search result diversification

Documents