Allison Chaney will present her research seminar/general exam on Tuesday October 15 at 2PM in Room 301 (note room). The members of her committee are: David Blei (advisor), Rebecca Fiebrink, and Andrea LaPaugh. Everyone is invited to attend her talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Her abstract and reading list follow below. ----- Original Message ----- Abstract 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 overall 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. Papers 1. D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation . Journal of Machine Learning Research , 3:993–1022, January 2003. http://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf 2. C. Wang and D. Blei. Collaborative topic modeling for recommending scientific articles. Knowledge Discovery and Data Mining , 2011. http://www.cs.cmu.edu/~chongw/papers/WangBlei2011.pdf 3. P. Gopalan, Scalable Recommendation with Poisson Factorization (forthcoming, supplied on request) 4. S. Purushotham, Y. Liu, and C.-C. J. Kuo. Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems . International Conference on Machine Learning, 2012. http://icml.cc/2012/papers/407.pdf 5. M. Hao, Y. Haixuan, M. R. Lyu , I. King. SoRec: Social Recommendation Using Probabilistic Matrix Factorization . Proceedings of the 17th ACM conference on Information and knowledge management , pg. 931–940, October 2008. http://appsrv.cse.cuhk.edu.hk/~hma/Paper_CIKM08_SoRec_Hao.pdf 6. H. Shan and A. Banerjee, Generalized Probabilistic Matrix Factorizations for Collaborative Filtering http://www-users.cs.umn.edu/~shan/icdm10_gpmf.pdf 7. Y. Koren, R. Bell, and C. Volinsky. Matrix Factorization Techniques for Recommender Systems . Computer , 42:30–37, August 2009. http://www2.research.att.com/~volinsky/papers/ieeecomputer.pdf 8. J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating Collaborative Filtering Recommender Systems . ACM Transactions on Information Systems , 2004. http://dl.acm.org/citation.cfm?doid=963770.963772 9. M. Hoffman, D. Blei, J. Paisley, and C. Wang. Stochastic variational inference . Journal of Machine Learning Research , 2013. http://www.cs.princeton.edu/~blei/papers/HoffmanBleiWangPaisley2013.pdf Selected Sections of Textbooks 1. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach , 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 2. Bishop, C. M., Pattern Recognition and Machine Learning , Springer , 2006 : 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 3. A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. Bayesian Data Analysi s, Taylor & Francis, 1995: chapters 5, 6, 11 4. Spall, J. C., Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control , Wiley, 2003: Ch 2 5. J. Lazar, J. Feng, and H. Hochheiser. Research Methods in Human-Computer Interaction. John Wiley & Sons, 2010: Chapters 2,3,10-12 6. R ecommender Systems Handbook, Springer , 2010. Chapter 8: Evaluating Recommendation Systems by G. Shani and A. Gunawardana.