automatic detection of navigational queries according to behavioural characteristics

Automatic detection of navigational queries according to Behavioural Characteristics

Automatic detection of navigational queriesaccording to Behavioural Characteristics

David J. Brenes MartínezDaniel Gayo-Avello

Lernen-Wissen-Adaptivitat (Learning-Knowledge-Adaptability) 08Workshop on Information Retrieval

Würzburg

2008/10/07


Structure of the presentation

1 Context of this research

2 Previous Research

3 Methodology

4 Coefficients

5 Conclussions

6 Future Research


Context of this research



2 Previous Research

3 Methodology

4 Coefficients

5 Conclussions

6 Future Research



Master on Web Engineering

Research specialityMaster Thesis



Research on Web Information Retrieval

How do the users search?Why do they search that way?How can we help them in their search tasks?


Previous Research

Previous Research


2 Previous Research

3 Methodology

4 Coefficients

5 Conclussions

6 Future Research


Previous Research

Why are we researching this?

Previous Research

2 Previous ResearchWhy are we researching this?

What are they searching?What tasks do they perform?With what purpose?


Previous Research


Anomalous characteristics of Web users

Short queriesShort search sessionsFew examined results


Previous Research


So...

Why do they not behave as expected?


Previous Research

What are they searching?

Previous Research

2 Previous ResearchWhy are we researching this?What are they searching?

What tasks do they perform?With what purpose?


Previous Research

What are they searching?

Simple approach

Simple approachWhat subjects do the users search?

Popular terms and queriesSubject taxonomy

Very useful in Advertising Research


Previous Research

What tasks do they perform?

Previous Research

2 Previous ResearchWhy are we researching this?What are they searching?What tasks do they perform?

With what purpose?


Previous Research

What tasks do they perform?

Tasks on a query

Different tasksAdding termsModifying termsDeleting terms...

Adaptation of IRSHelps for adding terms or modifying queriesDeleting terms?

Abstract tasks


Previous Research

With what purpose?

Previous Research

2 Previous ResearchWhy are we researching this?What are they searching?What tasks do they perform?With what purpose?


Previous Research

With what purpose?

Motivation underlying the query

Accesing some authority in a subjectUse of Web toolsPerforming some actions (shopping, downloading...)Problem resolutionSpare time


Previous Research

With what purpose?

IRS scenaries of use

Specific helpsDifferent needsDifferent characteristics?


Previous Research

With what purpose?

Intention Classification

TaxonomiesCommon in previous IR ResearchBroder

Navigational:google, cnn, apple store

Informational:Large hadron collider, Trains to Würzburg

Transactional:buy concert tickets, download torrents movies


Previous Research

With what purpose?

Classification Attempts

Manual classificationSimplerLess dataLexical and semantic characteristics

Length of the queryMeaning of the termsBiases

Performed by human expertsResults infuencied by the expertContradictions

Automated classificationMore dataMore complexLexical and semantic characteristicsBehaviour of the user


Previous Research

With what purpose?

Summary

Search characteristics points out differences for Web usersSubjects don’t justify these differenciesTasks performed on queries don’t clarify the reasonClassification based on lexic and semantics introducesome biases


Methodology

Methodology


2 Previous Research

3 Methodology

4 Coefficients

5 Conclussions

6 Future Research


Methodology

Data and tools

Methodology

3 MethodologyData and tools

Navigational CoefficientsPublishing


Methodology

Data and tools

AOL Query Log

AOL Query LogAnalysis performed over the whole query log

PythonPostgreSQL


Methodology

Navigational Coefficients

Methodology

3 MethodologyData and toolsNavigational Coefficients

Publishing


Methodology


Definition

‘Navigational’ criteriaBased on user’s behaviourTranslated to some statistical characteristic


Methodology


Presentation

Expected behaviourFormulaPros and consTOP 10 queries


Methodology

Publishing

Methodology

3 MethodologyData and toolsNavigational CoefficientsPublishing


Methodology

Publishing

Publishing data and results

Science repeatabilityWebsite of the author

http://www.davidjbrenes.infoDelayed publishing due to technical errorsNow it’s just my spanish blog :)


Coefficients

Coefficients


2 Previous Research

3 Methodology

4 Coefficients

5 Conclussions

6 Future Research


Coefficients

Most significant result

Coefficients

4 CoefficientsMost significant result

Number of resultsPercentage of navigational sessions


Coefficients


Expected behaviour

Traffic concentrated on one resultStrong relationship between query and resultQuery as ‘name’ of the resultProposed by Lee et al (2005)


Coefficients


Proposed formula

NC =Number_of_visits_most_popular_result

Number_of_visits_to_results

Rate of visits to the most visited result.


Coefficients


Pros and Cons

SimpleIntuitiveBad behaviour in the long tail (high values)Ignore distribution of results


Coefficients


Results

drudge retortsoulfuldetroitcosmology bookttologin.comjjj’s thumbnailgallery postbeteagleyscufrumsupportcricketnext.commsitf

Few performed queriesNon typical navigationalqueriesInfluence of individualusers


Coefficients

Number of results

Coefficients

4 CoefficientsMost significant resultNumber of results

Percentage of navigational sessions


Coefficients

Number of results

Expected behaviour

PolysemySame query means differents web sites for different usersVersions of a websiteNavigational behaviour for each result


Coefficients

Number of results

Proposed formula

NC = 1 − Number_of_distinct_resultsNumber_of_visits_to_results


Coefficients

Number of results

Pros and Cons

Focused on distribution of resultsFavor the most popular queriesBad behaviour on long tail (low values)


Coefficients

Number of results

Results

googleyahoo.commapquestyahooebay

google.combank of americawww.google.comwww.yahoo.comyahoo mail

Popular queriesTypical navigationalqueriesLexical and semanticcharacteristics similarto other researches


Coefficients


Coefficients

4 CoefficientsMost significant resultNumber of resultsPercentage of navigational sessions


Coefficients


Expected behaviour

A result satisfy the informational needNavigational queries isolated in a session


Coefficients


Proposed Formula

NC =Number_of_navigational_sessions

Number_of_sessions_of_query

Navigational SessionOne queryOne result


Coefficients


Pros and cons

Don’t favor the most popular queriesStrong dependency on the detection sessions algorithmMultitask influenceBad behaviour on long tail

Not common for these queries having a navigationalsessionLess important impact


Coefficients


Results

natural gas futurescashbreak.comallstar puzzlestimes enterpriseinstapunditclarksville leafchroniclefirst charter onlinemission viejo nadadorescounty of san joaquinbooking logthomas myspace editorbeta

Few performed queriesNon typical navigationalqueriesLexical and semanticcharacteristics similarto other researches

Possibly ignored byhuman experts


Conclussions

Conclussions


2 Previous Research

3 Methodology

4 Coefficients

5 Conclussions

6 Future Research


Conclussions

Comparison

Conclussions

5 ConclussionsComparison

Lexical and semantic characteristicsRelationship between statistics and behaviour


Conclussions

Comparison

Comparison

A relevant result usually corresponds with a little set ofvisited resultsThe inverse relationship is not true


Conclussions

Comparison

Comparison

A relevant result or a small set of results doesn’t assurenavigational sessions


Conclussions

Comparison

Comparison

A high rate of navigational sessions usually impliesrelevant results and a small set of results


Conclussions

Comparison

NC Combination

NC = NC_3∗NC_1 + NC_22

High weight forcoefficient dependingon navigationalsessionsAveraging of firstcoefficients

soulfuldetroitaol people magazine

cashbreak.comallstar puzzlesfirst charter

onlinemission viejo

nadadoresinstapundit

times enterpriseclarksville leaf

chronicleel canario

by the lagoon


Conclussions

Comparison

Lack of navigational sessions

Lowest values for NCNavigational queries wrongly grouped with another queriesMultitask influenceUse of NC to detect navigational sessions


Conclussions

Lexical and semantic characteristics

Conclussions

5 ConclussionsComparisonLexical and semantic characteristics

Relationship between statistics and behaviour


Conclussions



Some queries present themResearch is not based on those characteristicsResults are not biased

Some queries could have not been detectedDatabase of relevant termsURL queriesLong queries


Conclussions


Conclussions

5 ConclussionsComparisonLexical and semantic characteristicsRelationship between statistics and behaviour


Conclussions



Intention inferred from statistical characteristicsAutomatic evaluationTheorically extensible


Future Research

Future Research


2 Previous Research

3 Methodology

4 Coefficients

5 Conclussions

6 Future Research


Future Research

Combination of NC

Analysis of obtained resultsResearch on statistics metodologies for combinating them


Future Research

Another user’s intentions

Analysis of expected behaviourExperiments for each behaviour


Future Research

Detection of navigational sessions

Feedback for detection sessions algorithmCaution with biases (sessions artifitially created)Combination with others detection algorithms


And now...

Any question, please?

automatic detection of navigational queries according to behavioural characteristics

Education

research research

queries dont

research context

research master

advertising research

search tasks

abstract tasks

popular terms