Open AccessNeural Metric Matrix Factorization
Author(s) -
Boran Zheng,
Mingzhi Mao
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/768/5/052077
Subject(s) - metric (unit) , matrix decomposition , matrix (chemical analysis) , euclidean distance , collaborative filtering , computer science , factorization , artificial neural network , product (mathematics) , dot product , euclidean geometry , triangle inequality , non negative matrix factorization , algebra over a field , algorithm , artificial intelligence , mathematics , theoretical computer science , recommender system , machine learning , discrete mathematics , pure mathematics , engineering , eigenvalues and eigenvectors , physics , operations management , materials science , quantum mechanics , composite material , geometry
Nowadays, matrix factorization(MF) has been widely adopted in industry and research as a classical collaborative filtering (CF) algorithm. Unfortunately, the dot product adopted by matrix factorization is against the triangle inequality, which is one of the main reasons why this model is opposed. To address the issue, we propose a recommendation algorithm combining metric learning and mf in the paper. It transforms users’ preferences into distances, using Euclidean distance which satisfies triangular inequalities instead of traditional dot products, then directly decomposes the distance matrix into latent factor matrices of users and items. A multi-layer feedforward neural network is adopted for learning the model. Extensive experiments on two real-world datasets show that the proposed model is obviously superior to some advanced models based on matrix factorization.