1. WebMining Projectwork How to suggest the query youd like to
input after 2. The WebLog AnonID Query QueryTime ItemRank ClickURL
142rentdirect.com 01/03/2006 07:17 142www.prescriptionfortime.com
12/03/2006 12:31 142staple.com 17/03/2006 21:19 142staple.com
17/03/2006 21:19 142www.newyorklawyersite.com 18/03/2006 08:02
142www.newyorklawyersite.com 18/03/2006 08:03 142westchester.gov
20/03/2006 03:55 1 http://www.westchesterg ov.com 142space.comhttp
24/03/2006 20:51 The WebLog is AOL weblog made available to public
in 2006 3. The goal Building a query suggestion application
exploting the information observed on the AOL WebLog. Constrains:
1) the application relies on observed queries 2) The application
needs to be fast! 4. The approach Exploiting the relation between
typed queries and clicked URL by AOL users: If two queries share a
lot or URLs then they are strongly related to each other 5. a lot
of URLs. Several approaches can be followed for linking observed
queries to clicked URLs Weve been inspired by Query-URL Bipartite
Based Approach to Personalized Query Recommendation paper by Li,
Yang, Liu, Kitsuregawa, Proceedings of the Twenty-Third AAAI
Conference on Artificial Intelligence (2008) 6. Idea 1/2 Let q(i)
be the i-th query and u(k) be the k-th clicked url after a query is
typed A Bipartite Graph can be built such that for each q(i)
belonging to the query set, a link to a subsequent clicked url u(k)
can be defined 7. Idea 2/2 Once a Bipartite Graph has been built, a
relation between any query belonging to the query set can be
established accordingly to the clicked URLs. An Affinity Graph over
the query set can be defined consequently, where the edges between
two queries have to be weighted in order to exploit it in a
suggestion task 8. Weighting the Edges , = = () () = () + = () Let
q(i) be the i-th query and u(k) be the k-th clicked url after a
query is typed w(i,j) is equal to 1 if once q(i) or q(j) are passed
the same URLs are clicked w(i,j) is equal to 0 if once q(i) or q(j)
are passed, all the clicked URLs dont match 9. Managing
over-clicked URLs On the AOL 2006 WebLog dataset there exist a
number of URLs which are over-clicked by users, independently of
the query they type before clicking them. 0 20000 40000 60000 80000
100000 120000 140000 160000 180000 -foot-and-mouth-
http://books.stores.ebay.ie http://dixonmayfair.com
http://grounds-mag.com http://local.infospace.com
http://p072.ezboard.com http://shop.treonauts.com
http://vipcams.literotica.com http://www.acbarandgrill.com
http://www.alyandaj.com http://www.assplundering.com
http://www.beardieagilitydie http://www.bodo.com
http://www.calnhs.org http://www.chantcd.com
http://www.clubunlimited.com http://www.creativeforecasti
http://www.dennys.com http://www.duplicolor.com
http://www.esilvercart.com http://www.fitzandfloyd.com
http://www.gamecubecheats http://www.grandmashandsb
http://www.henrymedical.com http://www.i-m-t.demon.co.uk
http://www.jacksonsoccer.com http://www.keyloggers.com
http://www.leesburg2day.com http://www.madison.k12.ky.us
http://www.mercy.net http://www.mp3sugar.com http://www.netads.com
http://www.oceanviewinnan http://www.partsforlifts.com
http://www.poetsgraves.co.uk http://www.radio-3.ru
http://www.robotstorehk.com http://www.scotfest.com
http://www.skinashoba.com http://www.starktaxes.com
http://www.talktorusty.com http://www.theremyreport.c
http://www.trollcarnival.com http://www.vcta.com
http://www.welovedolls.com http://www.xandocosi.com URLs Click
Count 10. Managing over-clicked URLs Those URLS generate a noise in
the query recommendation algorithm. For this reason we selected
only those URLs having less than 1,000 clicks 0 100 200 300 400 500
600 700 800 900 1000 -foot-and-mouth-
http://blackdicksmovies.deluxep http://dallasnative.com
http://freescreensaver.ezthemes http://jingdong.en.alibaba.com
http://mtv-spring- http://pub25.bravenet.com http://store.vegas.com
http://westsideconnection.org http://www.acsu.buffalo.edu
http://www.amarula.com http://www.asht.org
http://www.bathandmore.com http://www.blackmanlaw.com
http://www.buerge.com http://www.caswells.com http://www.chsb.org
http://www.colts.com http://www.ctahperd.org
http://www.dewattoport.com http://www.dvdworldonline.com
http://www.ericdaugherty.com http://www.findlayfpc.org
http://www.frugalhaus.com http://www.gniarmls.com
http://www.hankingroup.com http://www.homerwood.com
http://www.incomemax.com http://www.jesusandkidz.com
http://www.kinray.com http://www.lemassif.com
http://www.machinetools.net.tw http://www.medrekforum.com
http://www.montgomerycollege. http://www.natalbelo.com
http://www.northlouisianaskydiv http://www.orientvisual.com
http://www.performancedogsina http://www.pptbackgrounds.fsn
http://www.ravc.com http://www.rodssteak- http://www.scms.ca
http://www.simplysiestakey.com http://www.sportsstats.com
http://www.supersprings.com http://www.thebeverlyhillscouri
http://www.tombraidermovie.com http://www.ulqini.de
http://www.virtualict.com http://www.whipnspur.com
http://www.yardleylondon.com URLs Click Count 11. Affinity Graph
Representation Once the edge weight is computed, for each query
q(i) we built a main dictionay having key = q(i) and value equal to
an ordered dictionary. The ordered dictionary has keys equals to
the queries sharing at least 1 URL with q(i) and values equal to
w(i,j). The main dictionary is used to feed the query suggestion
API and provide a reliable result in milliseconds. 12. Demo for
those who cant enjoy it the LIVE one 13. Thanks! Andrea Gigli
https://about.me/andrea.gigli
