Matrix Factorization Based Recommendation System using Hybrid Optimization Technique
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 indiv...
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European Alliance for Innovation (EAI)
2021-09-01
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Online Access: | https://eudl.eu/pdf/10.4108/eai.19-2-2021.168725 |
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doaj-d8e50be6707745d6ac1df8f9507c13e92021-09-29T07:04:46ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Energy Web2032-944X2021-09-0183510.4108/eai.19-2-2021.168725Matrix Factorization Based Recommendation System using Hybrid Optimization TechniqueP. Rao0T.V. Rao1Suresh Kurumalla2Bethapudi Prakash3Associate Professor of CSE, MVGR College of Engineering, Vizianagaram, Andhrapradesh, IndiaDepartment of CSE, Vignan’s Institute of Information Technology, Visakhapatnam, IndiaDepartment of CSE, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, IndiaDepartment of IT, Vignan's Institute of Engineering for Women, Visakhapatnam, IndiaIn 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).https://eudl.eu/pdf/10.4108/eai.19-2-2021.168725matrix factorizationalssgdoptimizationrecommendation systemlatent factorcollaborative filtering |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
P. Rao T.V. Rao Suresh Kurumalla Bethapudi Prakash |
spellingShingle |
P. Rao T.V. Rao Suresh Kurumalla Bethapudi Prakash Matrix Factorization Based Recommendation System using Hybrid Optimization Technique EAI Endorsed Transactions on Energy Web matrix factorization als sgd optimization recommendation system latent factor collaborative filtering |
author_facet |
P. Rao T.V. Rao Suresh Kurumalla Bethapudi Prakash |
author_sort |
P. Rao |
title |
Matrix Factorization Based Recommendation System using Hybrid Optimization Technique |
title_short |
Matrix Factorization Based Recommendation System using Hybrid Optimization Technique |
title_full |
Matrix Factorization Based Recommendation System using Hybrid Optimization Technique |
title_fullStr |
Matrix Factorization Based Recommendation System using Hybrid Optimization Technique |
title_full_unstemmed |
Matrix Factorization Based Recommendation System using Hybrid Optimization Technique |
title_sort |
matrix factorization based recommendation system using hybrid optimization technique |
publisher |
European Alliance for Innovation (EAI) |
series |
EAI Endorsed Transactions on Energy Web |
issn |
2032-944X |
publishDate |
2021-09-01 |
description |
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). |
topic |
matrix factorization als sgd optimization recommendation system latent factor collaborative filtering |
url |
https://eudl.eu/pdf/10.4108/eai.19-2-2021.168725 |
work_keys_str_mv |
AT prao matrixfactorizationbasedrecommendationsystemusinghybridoptimizationtechnique AT tvrao matrixfactorizationbasedrecommendationsystemusinghybridoptimizationtechnique AT sureshkurumalla matrixfactorizationbasedrecommendationsystemusinghybridoptimizationtechnique AT bethapudiprakash matrixfactorizationbasedrecommendationsystemusinghybridoptimizationtechnique |
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1716864586277191680 |