Type of Talk: Yahoo Data Mining Series

Speaker/ Affiliatio

n: Deepak Agarwal/ Yahoo! Labs

Date/ Time: September 16, 2009/ 11:00

am

Location: Avaya Auditorium, ACES 2.302

Host: Joydeep Ghosh

Talk Title: Statistical Challenges in Recommender System for Web Applica

tions

Talk Abstract:
In this talk, I will begin with an overview of
statistical challenges that arise in recommender system problems for web a

pplications like content optimization, online advertising. I will then des

cribe some modeling solutions for a content optimization problem that arise

s in the context of the Yahoo! Front Page. In particular, I will discuss t

ime series models to track item popularity, explore-exploit/sequential des

ign schemes to enhance performance and matrix factorization models to perso

nalize content to users. For some of the methods, I will present experimen

tal results from an actual system at Yahoo!. I will also provide examples o

f other applications where the techniques are useful and end with discussio

n of some open problems in the area.

Speaker Bio:
Dr. Deepak Agarwal
is currently a principal research scientist at Yahoo! Research. Prior to j

oining Yahoo!, he was a member of the statistics department at AT&T Resear

ch. His current research interests are on scalable statistical models for r

ecommender systems and online advertising. In particular, he is interested
in hierarchical modeling, sequential designs and matrix factorization met

hods. His other research interests include mining massive graphs, statisti

cal models for social network analysis, anomaly detection using a time ser

ies approach and spatial scan statistic for detecting hotspots.

Deepa

k has been a co-author on three best paper awards (Joint Statistical Meetin

gs, 2001; Siam Data Mining 2004; KDD 2007) in the past, he is currently
associate editor for Journal of American Statistical Association and regul

arly serves on program committees of data mining and machine conferences li

ke KDD, WWW, SDM, ICDM, WSDM, ICML and NIPS.