August 16, 2018

Continued Intern Project, Microsoft Research Lab - India, Bengaluru, India

We propose Clustered Monotone Transforms for Rating Factorization (CMTRF), a novel approach to perform regression up to unknown monotonic transforms over unknown population segments. For recommendation systems, the technique searches for monotonic transformations of the rating scales resulting in a better fit. This is combined with an underlying matrix factorization regression model that couples the user-wise ratings to exploit shared low dimensional structure. The rating scale transformations can be generated for each user (N-CMTRF), for a cluster of users (CMTRF), or for all the users at once (1-CMTRF), forming the basis of three simple and efficient algorithms proposed, all of which alternate between transformation of the rating scales and matrix factorization regression. Despite the non-convexity, CMTRF is theoretically shown to recover a unique solution under mild conditions.