Low-rank matrix factorization with attributes

Details Low-rank matrix factorization with attributes Low-rank matrix factorization with attributes

2006-11-24

arxiv.org/abs/cs/0611124v1

Computer Science

Learning

12 pages, 2 figures

Scientific paper

We develop a new collaborative filtering (CF) method that combines both previously known users' preferences, i.e. standard CF, as well as product/user attributes, i.e. classical function approximation, to predict a given user's interest in a particular product. Our method is a generalized low rank matrix completion problem, where we learn a function whose inputs are pairs of vectors -- the standard low rank matrix completion problem being a special case where the inputs to the function are the row and column indices of the matrix. We solve this generalized matrix completion problem using tensor product kernels for which we also formally generalize standard kernel properties. Benchmark experiments on movie ratings show the advantages of our generalized matrix completion method over the standard matrix completion one with no information about movies or people, as well as over standard multi-task or single task learning methods.

Affiliated with

Also associated with

No associations

Rating

Low-rank matrix factorization with attributes does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Low-rank matrix factorization with attributes, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Low-rank matrix factorization with attributes will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-540010