Post Doc position in France (Brittany, Lannion), recommendation engines
"frankmf.meyer" <frankmf.meyer-/[email protected]> Fri, 18 Jul 2008 09:23:51 -0000
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--rkoepZNBw6egbz5bKsNGVo6M--tNAVtby7P3WOK Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable France T=E9l=E9com R&D offers a 1 year post doc position at France Telecom in Lannion=20 (Brittany, France) 2008/2009 Field of research Recommendation engines: hybrid and anytime engines Key words Data Mining, Collaborative filtering, Recommendation Engine, Content- based Recommendation Context Recommendation engines are well-known systems used on many=20 commercial websites (see annexe). They offer recommendations such=20 as "people who liked/bought this item also liked/bought these=20 items", or "we predict that you will rate this item 4 stars." Recommendation engines use 2 different techniques: o Techniques called collaborative filtering: these techniques=20 use the purchase histories or user browsing logs with algorithm such=20 as "basket analysis" (association rules) or methods such as k- nearest neighbours. o Techniques called content-based filtering: in such a case=20 one seeks to build a thematic profile (for instance, using key- words) which enables the system to score each item. The list of the=20 scored items is then presented to the user. The subject on the recommendation engines has two main axes: 1. Hybrid engine We are looking for the best of both worlds: an engine which can work=20 with both collaborative filtering and content-based filtering whilst=20 still providing good predictive and scalability performances. 2. "AnyTime" engine We are looking for an algorithm and a framework architecture which=20 enables the recommendation system to work "anytime" (the model=20 updates are in real time or incremental). Research Work The research work will have 3 parts: 1. State of the art First of all, you will have to make a state of the art of the=20 different technologies of hybrid or incremental recommendation=20 engines: types of learning method used, type of data used, bench and=20 performances=85. =20 2. Algorithms and evaluation Next you will have to define one or more innovative recommendation=20 engine algorithms suited to the aforementioned constraints. 3. Communication This work will consist in publishing international articles and/or=20 develop patents. Type of position and location - A one year temporary work contract for FT R&D (Orange Labs) - working in a research team of about 15 people - department: France Telecom / R&D / Tech / Easy / Tsi - location : Lannion (Brittany, C=F4tes d'Armor, France) - salary: approx 32 K Euros Education and skills required - knowledge in machine learning techniques - PhD in Computer Science, if possible in AI, Statistics,=20 Machine Learning or Signal Processing. - skill in programming: knowledge and experience in Java NB: CVs which not correspond to this profile will not be considered Useful supplementary skills - experience in the field of recommendation systems - experience in the field of Information Retrieval - Knowledge in Databases (MySQL=85) - experience of working in a team Application deadline Application for this position will be accepted until November 2008=20 at the latest. Contact Frank Meyer France Telecom R&D/TECH/EASY (LD128) 2 avenue Pierre Marzin 22307 Lannion Cedex=20 E-mail : [email protected] Telephone : +33 (0)2 96 05 28 89 http://www.francetelecom.com/rd R=E9f=E9rences - http://en.wikipedia.org/wiki/Collaborative_filtering - http://en.wikipedia.org/wiki/Recommendation_system - NetFlix Challenge : http://www.netflixprize.com/ (Adomavicius & Tuzhilin, 2005) Adomavicius, G., & Tuzhilin, A.=20 (2005). Toward the next generation of recommender systems: A survey=20 of the state-of-the-art and possible extensions. IEEE Transactions=20 on Knowledge and Data Engineering, 17(6), 734=96749. (Bell et al., 2007) Bell, R., Koren, Y., & Volinsky, C. (2007).=20 Modeling relationships at multiple scales to improve accuracy of=20 large recommender systems. In 13th ACM SIGKDD International=20 Conference on Knowledge Discovery and Data Mining (pp. 95=96104). New=20 York, NY, USA. ACM. (Candillier et al., 2007) Candillier, L., Meyer, F., & Boull=E9, M.=20 (2007). Comparing state-of-the-art collaborative filtering systems.=20 In Perner, P. (Ed.), 5th International Conference on Machine=20 Learning and Data Mining in Pattern Recognition (pp. 548=96562),=20 Leipzig, Germany. Springer Verlag. (Herlocker et al., 2004) Herlocker, J., Konstan, J., Terveen, L., &=20 Riedl, J. (2004). Evaluating collaborative filtering recommender=20 systems. In ACM Transactions on Information Systems, 22(1), 5=9653. (Melville et al., 2002) Melville, P., Mooney, R., & Nagarajan, R.=20 (2002). Content-boosted collaborative filtering for improved=20 recommendations. In 18th National Conference on Artificial=20 Intelligence (pp. 187-192). (Polcicova et al., 2000) Polcicova, G., Slovak, R., & Navrat, P.=20 (2000). Combining content-based and collaborative filtering. In=20 ADBIS-DASFAA Symposium (pp. 118-127). --rkoepZNBw6egbz5bKsNGVo6M--tNAVtby7P3WOK Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/htm= l4/strict.dtd"> <html> <head> </head> <body style=3D"background-color: #ffffff;"> <!--~-|**|PrettyHtmlStartT|**|-~--> <div id=3D"ygrp-mlmsg" style=3D"width:655px; position:relative;"> <div id=3D"ygrp-msg" style=3D"width: 470px; margin:0; padding:0 25px 0 0; f= loat:left; z-index:1;"> <!--~-|**|PrettyHtmlEndT|**|-~--> <div id=3D"ygrp-text"> <p>France T=E9l=E9com R&D<br> offers a 1 year post doc position at France Telecom in Lannion <br> (Brittany, France)<br> 2008/2009<br> <br> Field of research<br> Recommendation engines: hybrid and anytime engines<br> <br> Key words<br> Data Mining, Collaborative filtering, Recommendation Engine, Content-<br> based Recommendation<br> <br> Context<br> Recommendation engines are well-known systems used on many <br> commercial websites (see annexe). They offer recommendations such <br> as "people who liked/bought this item also liked/bought these <br> items", or "we predict that you will rate this item 4 stars."= ;<br> <br> Recommendation engines use 2 different techniques:<br> <br> o Techniques called collaborative filtering: these techniques <br> use the purchase histories or user browsing logs with algorithm such <br> as "basket analysis" (association rules) or methods such as k-<br= > nearest neighbours.<br> o Techniques called content-based filtering: in such a case <br> one seeks to build a thematic profile (for instance, using key-<br> words) which enables the system to score each item. The list of the <br> scored items is then presented to the user.<br> <br> The subject on the recommendation engines has two main axes:<br> <br> 1. Hybrid engine<br> <br> We are looking for the best of both worlds: an engine which can work <br> with both collaborative filtering and content-based filtering whilst <br> still providing good predictive and scalability performances.<br> <br> 2. "AnyTime" engine<br> <br> We are looking for an algorithm and a framework architecture which <br> enables the recommendation system to work "anytime" (the model <b= r> updates are in real time or incremental)<wbr>.<br> Research Work<br> <br> The research work will have 3 parts:<br> <br> 1. State of the art<br> First of all, you will have to make a state of the art of the <br> different technologies of hybrid or incremental recommendation <br> engines: types of learning method used, type of data used, bench and <br> performances=85<wbr>.<br> <br> 2. Algorithms and evaluation<br> Next you will have to define one or more innovative recommendation <br> engine algorithms suited to the aforementioned constraints.<br> <br> 3. Communication<br> This work will consist in publishing international articles and/or <br> develop patents.<br> <br> Type of position and location<br> - A one year temporary work contract for FT R&D (Orange Labs)<br> - working in a research team of about 15 people<br> - department: France Telecom / R&D / Tech / Easy / Tsi<br> - location : Lannion (Brittany, C=F4tes d'Armor, France)<br> - salary: approx 32 K Euros<br> <br> Education and skills required<br> - knowledge in machine learning techniques<br> - PhD in Computer Science, if possible in AI, Statistics, <br> Machine Learning or Signal Processing.<br> - skill in programming: knowledge and experience in Java<br> NB: CVs which not correspond to this profile will not be considered<br> <br> Useful supplementary skills<br> - experience in the field of recommendation systems<br> - experience in the field of Information Retrieval<br> - Knowledge in Databases (MySQL=85)<br> - experience of working in a team<br> <br> Application deadline<br> <br> Application for this position will be accepted until November 2008 <br> at the latest.<br> <br> Contact<br> <br> Frank Meyer<br> France Telecom R&D/TECH/EASY (LD128)<br> 2 avenue Pierre Marzin 22307 Lannion Cedex <br> E-mail : <a href=3D"mailto:franck.meyer%40orange-ftgroup.com">franck.meyer@= <wbr>orange-ftgroup.<wbr>com</a><br> Telephone : +33 (0)2 96 05 28 89<br> <a href=3D"http://www.francetelecom.com/rd">http://www.francete<wbr>lecom.c= om/<wbr>rd</a><br> <br> R=E9f=E9rences<br> - <a href=3D"http://en.wikipedia.org/wiki/Collaborative_filtering">http://e= n.wikipedia<wbr>.org/wiki/<wbr>Collaborative_<wbr>filtering</a><br> - <a href=3D"http://en.wikipedia.org/wiki/Recommendation_system">http://en.= wikipedia<wbr>.org/wiki/<wbr>Recommendation_<wbr>system</a><br> - NetFlix Challenge : <a href=3D"http://www.netflixprize.com/">http://www.= netflixp<wbr>rize.com/</a><br> <br> (Adomavicius & Tuzhilin, 2005) Adomavicius, G., & Tuzhilin, A. <br> (2005). Toward the next generation of recommender systems: A survey <br> of the state-of-the-<wbr>art and possible extensions. IEEE Transactions <br= > on Knowledge and Data Engineering, 17(6), 734=96749.<br> <br> (Bell et al., 2007) Bell, R., Koren, Y., & Volinsky, C. (2007). <br> Modeling relationships at multiple scales to improve accuracy of <br> large recommender systems. In 13th ACM SIGKDD International <br> Conference on Knowledge Discovery and Data Mining (pp. 95=96104). New <br> York, NY, USA. ACM.<br> <br> (Candillier et al., 2007) Candillier, L., Meyer, F., & Boull=E9, M. <br= > (2007). Comparing state-of-the-<wbr>art collaborative filtering systems. <b= r> In Perner, P. (Ed.), 5th International Conference on Machine <br> Learning and Data Mining in Pattern Recognition (pp. 548=96562), <br> Leipzig, Germany. Springer Verlag.<br> <br> (Herlocker et al., 2004) Herlocker, J., Konstan, J., Terveen, L., & <br= > Riedl, J. (2004). Evaluating collaborative filtering recommender <br> systems. In ACM Transactions on Information Systems, 22(1), 5=9653.<br> <br> (Melville et al., 2002) Melville, P., Mooney, R., & Nagarajan, R. <br> (2002). Content-boosted collaborative filtering for improved <br> recommendations. In 18th National Conference on Artificial <br> Intelligence (pp. 187-192).<br> <br> (Polcicova et al., 2000) Polcicova, G., Slovak, R., & Navrat, P. <br> (2000). Combining content-based and collaborative filtering. In <br> ADBIS-DASFAA Symposium (pp. 118-127).<br> <br> </p> </div>=20=20 <!--~-|**|PrettyHtmlStart|**|-~--> <span width=3D"1" style=3D"color: white;">__._,_.___</span> <!-- Start the section with Message In topic --> <div id=3D"ygrp-actbar"> <span class=3D"left"> <a href=3D"http://groups.yahoo.com/group/webir/message/2549;_ylc= =3DX3oDMTM0N3VicjJsBF9TAzk3MzU5NzE0BGdycElkAzEyMzQ1ODUEZ3Jwc3BJZAMxNzA1MDgz= MjA2BG1zZ0lkAzI1NDkEc2VjA2Z0cgRzbGsDdnRwYwRzdGltZQMxMjE2NTU0NjI1BHRwY0lkAzI= 1NDk-"> Messages in this topic </a> (<span class=3D"bld">1</sp= an>) </span> <a href=3D"http://groups.yahoo.com/group/webir/post;_ylc=3DX3oDMTJw= a2hwMmt2BF9TAzk3MzU5NzE0BGdycElkAzEyMzQ1ODUEZ3Jwc3BJZAMxNzA1MDgzMjA2BG1zZ0l= kAzI1NDkEc2VjA2Z0cgRzbGsDcnBseQRzdGltZQMxMjE2NTU0NjI1?act=3Dreply&messageNu= m=3D2549"> <span class=3D"bld"> Reply </span> (via web post) </a> |=20 <a href=3D"http://groups.yahoo.com/group/webir/post;_ylc=3DX3oDMTJl= dmo1MjRwBF9TAzk3MzU5NzE0BGdycElkAzEyMzQ1ODUEZ3Jwc3BJZAMxNzA1MDgzMjA2BHNlYwN= mdHIEc2xrA250cGMEc3RpbWUDMTIxNjU1NDYyNQ--" class=3D"bld"> Start a new topic </a> </div>=20 <!------- Start Nav Bar ------> <!-- |**|begin egp html banner|**| --> <div id=3D"ygrp-vitnav"> <a href=3D"http://groups.yahoo.com/group/webir/messages;_yl= c=3DX3oDMTJlaDdnZzAzBF9TAzk3MzU5NzE0BGdycElkAzEyMzQ1ODUEZ3Jwc3BJZAMxNzA1MDg= zMjA2BHNlYwNmdHIEc2xrA21zZ3MEc3RpbWUDMTIxNjU1NDYyNQ--">Messages</a>=20=20 =20=20=20=20=20=20=20=20 =20=20=20=20=20=20=20=20 =20=20=20=20=20=20=20=20 =20=20=20=20=20=20=20=20 =20=20=20=20=20=20=20=20 =20=20=20=20=20=20=20=20 =20=20=20=20=20=20=20=20 </div>=20=20 <!-- |**|end egp html banner|**| --> <div id=3D"ygrp-grft"> =20=20=20=20=20=20=20=20=20=20 </div> =20=20=20=20=20=20 <div id=3D"ygrp-mkp"> <div id=3D"hd">MARKETPLACE</div> <div id=3D"ads"> <div class=3D"ad"> <hr size=3D1 noshade><a href=3D"http://us.ard.yahoo.com/SIG=3D1= 3rt99eo7/M=3D624381.12730922.13032918.10835568/D=3Dgroups/S=3D1705083206:MK= P1/Y=3DYAHOO/EXP=3D1216561825/L=3D/B=3DYfQxAULaX9k-/J=3D1216554625955908/A= =3D5379723/R=3D0/SIG=3D14evd63no/*http://media.adrevolver.com/adrevolver/hr= ef?banner=3D189158&place=3D26143&url_=3Dhttp://tc.deals.yahoo.com/tc/blockb= uster/display.com?cid=3Dbbi00027">Blockbuster</a> is giving away a FREE tri= al of - <a href=3D"http://us.ard.yahoo.com/SIG=3D13rt99eo7/M=3D624381.12730= 922.13032918.10835568/D=3Dgroups/S=3D1705083206:MKP1/Y=3DYAHOO/EXP=3D121656= 1825/L=3D/B=3DYfQxAULaX9k-/J=3D1216554625955908/A=3D5379723/R=3D1/SIG=3D14e= vd63no/*http://media.adrevolver.com/adrevolver/href?banner=3D189158&place= =3D26143&url_=3Dhttp://tc.deals.yahoo.com/tc/blockbuster/display.com?cid=3D= bbi00027">Blockbuster Total Access.</a> <!--AdRevolver code begin--> <script type=3D"text/javascript"> <!-- var title =3D 'TITLE'; try { if (title =3D=3D unescape('%u0054%u0049%u0054%u004C%u0045') && document.tit= le) title =3D document.title; var https =3D false; try {https =3D document.location.href.indexOf('https')= =3D=3D0}catch(e){} document.write('<img width=3D"0" height=3D"0" border=3D"0" src=3D"http' + (= https ? 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