Autumn 2011 1

Web Information retrieval (Web IR)Handout #13:

Ranking based on User BehaviorRanking based on User Behavior

Ali Mohammad Zareh BidokiECE Department, Yazd University

alizareh@yaduni.ac.ir

Autumn 2011 2

Finding Ranking Function

• R=f( Query, User behavior, web graph & content features)

• How can we use the user behavior?– Explicit– Implicit

• 80% of user clicks are related to query– Click-through data– From search Engines log

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Click-through data (by Joachims )

c q

r

• Click-through data– Triple (q,r,c)

• q=query• r=ranked list• c=set of clicked docs

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Benefits of Using Click through data

• Democracy in Web• Filling gap between user needs and

results• User clicks are more valuable that a

page content (Search engine precision is evaluated by user no page creators)

• Degree of relevancy between query and documents will increase (Adding click metadata to document)

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Web EntitiesWords

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Docs

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Docs

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Web graph

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Users

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q

Queries

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Document Expansion Using Click TD

• First time Google used Anchortext as a document content– Anchor text is view of a document from another

document

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Long term incremental learning

• Di vector of a document in ith iteration• Q is vector of the query that this document is

clicked• Alpha is learning rate

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Naïve Method (NM) A bipartite graph for docs and queries

• Mij is number of clicks on document j for query i

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Naïve Method (Cont.)

• The weight between query qj and document di:

• The meta data for document i is:

mimiii qwqwqwd ...2211

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Co-Visited Method

• If two pages are clicked by the same query they called co-visited.

• The similarity between two docs i and j is (visited(di) shows number of clicks on di and visited(di,dj) shows number of queries in which both are clicked):

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Co-Visited Disadvantages

• It only considers documents similarity (not query similarity)

• As users clicks on top 10 pages, click data are sparse (1.5 queries for each page)– So similarity is not precise

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Iterative Method (IM)

• O(q): set of clicked page for q• Oi(q): the ith clicked page for q• I(d): set of queries in which it is clicked on d• Ii(d): The ith query in which it is clicked on d

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Experimental Results

• Experimental results on a real large query click-through log, i.e. MSN query log data, indicate that the proposed algorithm relatively outperforms – the baseline search system by 157%, – naïve query log mining by 17% and – co-visited algorithm by 17%

• on top 20 precision respectively.