Matrix Factorization Based Recommendation System using Hybrid Optimization Technique

• Main Authors: P. Rao, T.V. Rao, Suresh Kurumalla, Bethapudi Prakash
• Format: Article
• Language: English
• Published: European Alliance for Innovation (EAI) 2021-09-01
• Series: EAI Endorsed Transactions on Energy Web
• Subjects: matrix factorization; als; sgd; optimization; recommendation system; latent factor; collaborative filtering
• Online Access: https://eudl.eu/pdf/10.4108/eai.19-2-2021.168725

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).