Print

Print


[Apologies for multiple postings]

Please notice the unusual time/venue. Thursday 3pm (Nov 15)
in ENGR 4801.

******************************************************************
*
*
*    GRAND Seminar
*
*    http://cs.gmu.edu/~robotics/**pmwiki.php/Main/GrandSeminar<http://cs.gmu.edu/~robotics/pmwiki.php/Main/GrandSeminar>
*
*
******************************************************************


*Title*

Sparse Linear Methods for Top-N Recommender Systems

*Time/Venue*

Nov. 15, 3pm, Thursday, 2012
ENGR 4801

*Speaker*

Xia Ning
Research staff
Autonomic Management Department
NEC Labs America

*Host*

Huzefa Rangwala

*Abstract*

Recommender systems represent a set of computational methods that
produce recommendations of interesting entities (e.g., products,
friends, etc) from a large collection of such entities by
retrieving/filtering/learning information from their own properties
(e.g., product attributes, personal profiles, etc) and/or the
interactions between these entities and other parties (e.g.,
user-product ratings, friend-friend trust relations, etc). Recommender
systems are particularly important for E-commerce applications, where
the overwhelming amount of items makes it extremely difficult for
users to manually identify those items that best fit their personal
preferences. Recommender systems have attracted significant research
interests from academia, and meanwhile, they have been functioning as
a critical revenue enhancer for major E-commerce websites such as
Amazon.com and eBay.com.

In this talk, we will address a core task for recommender systems,
that is, top-N recommendation, in which a size-N list of items that
most conform to the user's interests and preference is to be generated
for recommendation. We have developed 1). a novel sparse linear method
for top-N recommendation, which utilizes regularized linear regression
with sparsity constraints to model user-item purchase patterns, and it
significantly outperforms the current state-of-the-art methods; 2). a
set of novel sparse linear methods with side information for top-N
recommendation, which use side information to regularize sparse linear
models or use side information directly to model user-item purchase
behaviors, and they stand for the best performing methods with side
information incorporated. These sparse linear methods are particularly
suitable for Big-Data environment, where the huge amount of
information leaves the conventional computation-intensive
recommendation algorithms inapplicable, whereas the sparse linear
methods are easy to be distributed on large-scale computing systems
like Hadoop.

*Bio*

Dr. Xia Ning is a research staff member at the Autonomic Management
Department, NEC Labs America. Dr. Ning received her PhD in Computer
Science from the University of Minnesota, Twin Cities, 2012. Her
doctoral research focuses on recommender systems for large-scale
e-commerce applications and data mining/machine learning methods for
drug discovery and medical informatics. She has also been working on
applying and developing statistical analytics/data mining algorithms
for real “big-data” problems arising in text mining, social network
mining and autonomic system management.