Title

Matrix Factorization based Collaborative Filtering with Resilient Stochastic Gradient Descent

Abstract

One of the leading approaches to collaborative filtering is to use matrix factorization to discover a set of latent factors that explain the pattern of preferences. In this paper, we apply a resilient stochastic gradient descent approach that uses only the sign of the gradient, similar to the R-Prop algorithm in neural network training, to matrix factorization for collaborative filtering. We evaluate the performance of our approach on the MovieLens 1M dataset, and find that test set accuracy markedly improves compared to standard gradient descent. As a follow-up experiment, we apply clustering to the learned item-factor matrix in factor space, and attempt to manually characterize each cluster of movies.

Meeting Name

2018 International Joint Conference on Neural Networks, IJCNN 2018 (2018: Jul. 8-13, Rio de Janeiro, Brazil)

Department(s)

Electrical and Computer Engineering

Comments

Partial support from the Brandon University Research Council (BURC), the Missouri University of Science and Technology Intelligent Systems Center and the M.K. Finley Missouri Endowment is gratefully acknowledged.

Keywords and Phrases

Factorization; Matrix Algebra; Neural Networks; Statistical Tests; Stochastic Systems, Factor Space; Gradient Descent; Latent Factor; Matrix Factorizations; Movielens; Neural Network Training; Stochastic Gradient Descent; Test Sets, Collaborative Filtering

International Standard Book Number (ISBN)

978-1-5090-6014-6

International Standard Serial Number (ISSN)

2161-4407

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jul 2018

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