Research Article
Matrix Factorization Based Recommendation System using Hybrid Optimization Technique
@ARTICLE{10.4108/eai.19-2-2021.168725, author={P. Srinivasa Rao and T.V. Madhusudhana Rao and Suresh Kurumalla and Bethapudi Prakash}, title={Matrix Factorization Based Recommendation System using Hybrid Optimization Technique}, journal={EAI Endorsed Transactions on Energy Web}, volume={8}, number={35}, publisher={EAI}, journal_a={EW}, year={2021}, month={2}, keywords={matrix factorization, ALS, SGD, optimization, recommendation system, latent factor, collaborative filtering}, doi={10.4108/eai.19-2-2021.168725} }- P. Srinivasa Rao
T.V. Madhusudhana Rao
Suresh Kurumalla
Bethapudi Prakash
Year: 2021
Matrix Factorization Based Recommendation System using Hybrid Optimization Technique
EW
EAI
DOI: 10.4108/eai.19-2-2021.168725
Abstract
In this paper, a matrix factorization recommendation algorithm is used to recommend items to the user by inculcating a hybrid optimization technique that combines Alternating Least Squares (ALS) and Stochastic Gradient Descent (SGD) in the advanced stage and compares the two individual algorithms with the hybrid model. This hybrid optimization algorithm can be easily implemented in the real world as a cold start can be easily reduced. The hybrid technique proposed is set side-by-side with the ALS and SGD algorithms individually to assess the pros and cons and the requirements to be met to choose a specific technique in a specific domain. The metric used for comparison and evaluation of this technique is Mean Squared Error (MSE).
Copyright © 2021 P. Srinivasa Rao et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
