<html><head><style type='text/css'>p { margin: 0; }</style></head><body><div style='font-family: arial,helvetica,sans-serif; font-size: 10pt; color: #000000'>Allison Chaney will present her research seminar/general exam on Tuesday October 15 at <br>2PM in Room 301 (note room). The members of her committee are: David Blei (advisor), <br>Rebecca Fiebrink, and Andrea LaPaugh. Everyone is invited to attend her talk, and <br>those faculty wishing to remain for the oral exam following are welcome to do so. Her abstract<br>and reading list follow below.<br><br><hr id="zwchr"><div style="color:#000;font-weight:normal;font-style:normal;text-decoration:none;font-family:Helvetica,Arial,sans-serif;font-size:12pt;"><b></b><div dir="ltr"><div><font color="#232323" face="Arial"><b><u>Abstract</u></b></font></div><div><span style="font-family:arial,sans-serif;font-size:13px">Each
of us are faced with the problem of selecting which books to read and
movies to watch. Traditionally, we ask our trusted friends for
recommendations, but algorithmic recommendation models make those
choices even easier, saving us time and effort by steering us towards
media we are more likely to enjoy. The downside to most probabilistic
recommendations is that for some people, part of the appeal of reading
or consuming other media is in creating shared experiences with friends.
I present a model that incorporates social network information into
recommendation models, reintroducing the social aspect to
recommendation; this approach also has the potential to improve </span><span style="font-family:arial,sans-serif;font-size:13px">overall </span><span style="font-family:arial,sans-serif;font-size:13px">recommendations.
This model discovers the latent trust that exists between users in a
network and allows us to consider which users are more trustworthy than
others, providing us insight into the social network's dynamics.</span></div><div><br></div><div><span style="font-size:13px;font-family:Arial"></span><br>
<span style="font-size:13px;font-family:Arial;color:rgb(35,35,35)"><u><b>Papers</b></u></span><br><ol><li><span style="font-size:13px;font-family:Arial;color:rgb(35,35,35)">D. Blei, A. Ng, and M. Jordan. <b>Latent Dirichlet allocation</b>. <i>Journal of Machine Learning Research</i>, 3:993–1022, January 2003. </span><span style="font-size:13px;font-family:Arial;color:rgb(4,46,238)"><u><a href="http://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf" target="_blank">http://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf</a></u></span></li>
<li><span style="font-size:13px;font-family:Arial;color:rgb(35,35,35)">C. Wang and D. Blei. <b>Collaborative topic modeling for recommending scientific articles. </b><i>Knowledge Discovery and Data Mining</i>, 2011. </span><span style="font-size:13px;font-family:Arial;color:rgb(4,46,238)"><u><a href="http://www.cs.cmu.edu/~chongw/papers/WangBlei2011.pdf" target="_blank">http://www.cs.cmu.edu/~chongw/papers/WangBlei2011.pdf</a></u></span></li>
<li><span style="color:rgb(35,35,35);font-family:Arial;font-size:13px">P. Gopalan, </span><b style="color:rgb(35,35,35);font-family:Arial;font-size:13px">Scalable Recommendation with Poisson Factorization</b><i style="color:rgb(35,35,35);font-family:Arial;font-size:13px"> (forthcoming, supplied on request)</i></li>
<li><span style="font-size:13px;font-family:Arial;color:rgb(35,35,35)">S. Purushotham, Y. Liu, and C.-C. J. Kuo. <b>Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems</b>. <i>International Conference on Machine Learning, </i>2012. </span><span style="font-size:13px;font-family:Arial;color:rgb(18,85,204)"><u><a href="http://icml.cc/2012/papers/407.pdf" target="_blank">http://icml.cc/2012/papers/407.pdf</a></u></span></li>
<li><span style="font-size:13px;font-family:Arial;color:rgb(35,35,35)">M. Hao, Y. Haixuan, M. R. Lyu , I. King. <b>SoRec: Social Recommendation Using Probabilistic Matrix Factorization</b>. <i>Proceedings of the 17th ACM conference on Information and knowledge management</i>, pg. 931–940, October 2008. </span><span style="font-size:13px;font-family:Arial;color:rgb(18,85,204)"><u><a href="http://appsrv.cse.cuhk.edu.hk/~hma/Paper_CIKM08_SoRec_Hao.pdf" target="_blank">http://appsrv.cse.cuhk.edu.hk/~hma/Paper_CIKM08_SoRec_Hao.pdf</a></u></span></li>
<li><span style="font-size:13px;font-family:Arial;color:rgb(35,35,35)">H. Shan and A. Banerjee, <b>Generalized Probabilistic Matrix Factorizations for Collaborative Filtering </b></span><span style="font-size:13px;font-family:Arial;color:rgb(18,85,204)"><u><a href="http://www-users.cs.umn.edu/~shan/icdm10_gpmf.pdf" target="_blank">http://www-users.cs.umn.edu/~shan/icdm10_gpmf.pdf</a></u></span></li>
<li><span style="font-size:13px;font-family:Arial;color:rgb(35,35,35)">Y. Koren, R. Bell, and C. Volinsky. <b>Matrix Factorization Techniques for Recommender Systems</b>. <i>Computer</i>, 42:30–37, August 2009. </span><span style="font-size:13px;font-family:Arial;color:rgb(4,46,238)"><u><a href="http://www2.research.att.com/~volinsky/papers/ieeecomputer.pdf" target="_blank">http://www2.research.att.com/~volinsky/papers/ieeecomputer.pdf</a></u></span></li>
<li><span style="font-size:13px;font-family:Arial;color:rgb(35,35,35)">J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. <b>Evaluating Collaborative Filtering Recommender Systems</b>. <i>ACM Transactions on Information Systems</i>, 2004. </span><span style="font-size:13px;font-family:Arial;color:rgb(4,46,238)"><u><a href="http://dl.acm.org/citation.cfm?doid=963770.963772" target="_blank">http://dl.acm.org/citation.cfm?doid=963770.963772</a></u></span></li>
<li><span style="font-size:13px;font-family:Arial;color:rgb(35,35,35)">M. Hoffman, D. Blei, J. Paisley, and C. Wang. <b>Stochastic variational inference</b>. <i>Journal of Machine Learning Research</i>, 2013. </span><span style="font-size:13px;font-family:Arial;color:rgb(18,85,204)"><u><a href="http://www.cs.princeton.edu/~blei/papers/HoffmanBleiWangPaisley2013.pdf" target="_blank">http://www.cs.princeton.edu/~blei/papers/HoffmanBleiWangPaisley2013.pdf</a></u></span></li>
</ol>
<span style="font-size:13px;font-family:Arial;color:rgb(35,35,35)"><u><b>Selected Sections of Textbooks</b></u></span><br><ol><li><span style="color:rgb(35,35,35);font-family:Arial;font-size:13px">S. Russell and P. Norvig, </span><b style="color:rgb(35,35,35);font-family:Arial;font-size:13px">Artificial Intelligence: A Modern Approach</b><span style="color:rgb(35,35,35);font-family:Arial;font-size:13px">, Prentice Hall Series in Artificial Intelligence, 2003: Chapters: 3.1-3.5, 4.1, 5, 8.1-8.3, 13.1-13.5, 14, 15, 18, 21</span></li>
<li><span style="color:rgb(35,35,35);font-family:Arial;font-size:13px">Bishop, C. M., </span><b style="color:rgb(35,35,35);font-family:Arial;font-size:13px">Pattern Recognition and Machine Learning</b><span style="color:rgb(35,35,35);font-family:Arial;font-size:13px">, </span><i style="color:rgb(35,35,35);font-family:Arial;font-size:13px">Springer</i><span style="color:rgb(35,35,35);font-family:Arial;font-size:13px">, 2006</span><i style="color:rgb(35,35,35);font-family:Arial;font-size:13px">:</i><span style="color:rgb(35,35,35);font-family:Arial;font-size:13px"> Chapters 1, 2, 3.1-3.3, 4.1-4.3, 8, 9, 10, 11.1-11.3, 12.1-12.2, 13, 14.1, 14.3</span></li>
<li><span style="color:rgb(35,35,35);font-family:Arial;font-size:13px">A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. </span><b style="color:rgb(35,35,35);font-family:Arial;font-size:13px">Bayesian Data Analysi</b><span style="color:rgb(35,35,35);font-family:Arial;font-size:13px">s, </span><i style="color:rgb(35,35,35);font-family:Arial;font-size:13px">Taylor & Francis, </i><span style="color:rgb(35,35,35);font-family:Arial;font-size:13px">1995: chapters 5, 6, 11</span></li>
<li><span style="color:rgb(35,35,35);font-family:Arial;font-size:13px">Spall, J. C., </span><b style="color:rgb(35,35,35);font-family:Arial;font-size:13px">Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control</b><span style="color:rgb(35,35,35);font-family:Arial;font-size:13px">, </span><i style="color:rgb(35,35,35);font-family:Arial;font-size:13px">Wiley, </i><span style="color:rgb(35,35,35);font-family:Arial;font-size:13px">2003: Ch 2</span></li>
<li><span style="color:rgb(35,35,35);font-family:Arial;font-size:13px">J. Lazar, J. Feng, and H. Hochheiser. </span><b style="color:rgb(35,35,35);font-family:Arial;font-size:13px">Research Methods in Human-Computer Interaction.</b><span style="color:rgb(35,35,35);font-family:Arial;font-size:13px"> John Wiley & Sons, 2010: Chapters 2,3,10-12</span></li>
<li><font color="#232323" face="Arial"><b>R</b></font><span style="font-size:13px;font-family:Arial;color:rgb(35,35,35)"><b>ecommender Systems Handbook, </b><i>Springer</i>, 2010. Chapter 8: <i>Evaluating Recommendation Systems</i> by<b> </b>G. Shani and A. Gunawardana. </span></li>
</ol><div><font color="#232323" face="Arial"><br></font></div></div><br></div>
</div><br></div></body></html>