Statistics and Data Science Seminar
Junhui Wang
City University of Hong Kong
A smooth collaborative recommender system
Abstract: In recent years, there has been a growing demand to develop efficient recommender systems which track users' preferences and recommend potential
items of interest to users. In this talk, I will present a smooth collaborative
recommender system to utilize dependency information among users and
items which share similar characteristics under the singular value decomposition framework. The proposed method incorporates the neighborhood
structure among user-item pairs by exploiting covariates to improve the prediction performance. One key advantage of the proposed method is that it
leads to more efficient recommendation for "cold-start" users and items,
whose preference information is completely missing from the training set.
As this type of data involves large-scale customer records, efficient scheme
will be proposed to achieve scalable computing. The advantage is confirmed
in a variety of simulated experiments as well as one large-scale real example
on Last.fm music listening counts. If time permits, the asymptotic properties
will also be discussed.
Wednesday April 17, 2019 at 3:00 PM in 636 SEO