A Ternary Non-Commutative Latent Factor Model for Scalable Three-Way Real Tensor Completion
This is an incremental improvement for large-scale collaborative filtering applications.
The paper tackled the problem of scalable three-way tensor completion for collaborative filtering by introducing a Non-Commuting Latent Factor model, which outperformed the PARAFAC model on MovieLens and Fannie Mae datasets.
Motivated by large-scale Collaborative-Filtering applications, we present a Non-Commuting Latent Factor (NCLF) tensor-completion approach for modeling three-way arrays, which is diagonal like the standard PARAFAC, but wherein different terms distinguish different kinds of three-way relations of co-clusters, as determined by permutations of latent factors. The first key component of the algebraic representation is the usage of two non-commutative real trilinear operations as the building blocks of the approximation. These operations are the standard three dimensional triple-product and a trilinear product on a two-dimensional real vector space, which is a representation of the real Clifford Algebra Cl(1,1) (a certain Majorana spinor). Both operations are purely ternary in that they cannot be decomposed into two group-operations on the relevant spaces. The second key component of the method is combining these operations using permutation-symmetry preserving linear combinations. We apply the model to the MovieLens and Fannie Mae datasets, and find that it outperforms the PARAFAC model. We propose some future directions, such as unsupervised-learning.