Skip to content
image post
White papers

Feature normalization for similarity calculations in matrix factorization method

April 16, 2024

Share with:


Authors: Hoang Vu Dang

Abstract: Matrix factorization is a well-established approach in recommender systems with its roots in document retrieval. In this approach the original matrix of interactions between users and items is approximately factorized into matrices representing user features and item features. In this paper we argue for an orthogonality condition, under which the rows of the user feature matrix can be used to estimate similarity between users (and similarly for item similarity). Furthermore we provide an algebraic derivation of a normalization procedure to ensure that orthogonality conditions holds for any matrix factorization technique regardless of the rank of the factors. Finally we demonstrate the improvement in similarity ratings when the aforementioned normalization is applied to both explicit ratings and implicit feedback datasets, using the Alternating Least Square with Weighted λ-Regularization and Bayesian Probabilistic Matrix Factorization models.

Published in: 2018 10th International Conference on Knowledge and Systems Engineering (KSE)

Date of Conference: 1-3 Nov. 2018


Download now

Do you need a workthrough of our platform? Let us know

    Related Posts

    Get ahead with AI-powered technology updates!

    Subscribe now to our newsletter for exclusive insights, expert analysis, and cutting-edge developments delivered straight to your inbox!