Matrix Factorization Based Recommendation System using Hybrid Optimization Technique

Rao, P. Srinivasa and Rao, T.V. Madhusudhana and Kurumalla, Suresh and Prakash, Bethapudi (2021) Matrix Factorization Based Recommendation System using Hybrid Optimization Technique. EAI Endorsed Transactions on Energy Web. e22. ISSN 2032-944X

[thumbnail of eai.19-2-2021.168725.pdf]
Available under License Creative Commons Attribution No Derivatives.

Download (1MB) | Preview


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).

Item Type: Article
Uncontrolled Keywords: matrix factorization, ALS, SGD, optimization, recommendation system, latent factor, collaborative filtering
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 20 Jul 2021 09:51
Last Modified: 20 Jul 2021 09:51

Actions (login required)

View Item
View Item