Tensors for Multi-Dimensional Recommenders November 24, 2007Posted by Andre Vellino in Collaborative filtering, Digital library, Recommender service.
It’s good to know that the intellectual heavy-lifting is there when you need it. The ideas in Peter Turney‘s recent tech report on tensors may well be of some use to me when it comes to implementing the multi-dimensional component of the Synthese Recommender that Dave Zeber and I are developing at CISTI. The charts from our presentation at WPRS workshop at Web Intelligence give some indication of how we intend to build these multi-dimensional matrices.
This component of our system is for some time in the future, but I expect the resulting tensors may be a challenge even for Peter’s MATLAB code (given in the paper). Fortunately, I think this can be combined with another idea for distributing recommenders across different subject domains, as described by F. Ricci et al. at RecSys 2007. That’s one way of both reducing the dimensionality of the original tensors and parallelizing the computation, but I agree with Daniel Lemire observation that Peter’s code can be optimized for parallel processors as well.