Matrix Factorization Based Recommendation System using Hybrid Optimization Technique

被引：0

作者：

Rao P.S. ^{[1
]}

Rao T.V.M. ^{[2
]}

Kurumalla S. ^{[3
]}

Prakash B. ^{[4
]}

机构：

[1] CSE MVGR College of Engineering, Vizianagaram, Andhrapradesh

[2] Department of CSE, Vignan’s Institute of Information Technology, Visakhapatnam

[3] Department of CSE, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam

[4] Department of IT, Vignan's Institute of Engineering for Women, Visakhapatnam

来源：

EAI Endorsed Transactions on Energy Web | 2021年 / 8卷 / 35期

关键词：

ALS; collaborative filtering; latent factor; matrix factorization; optimization; recommendation system; SGD;

D O I：

10.4108/eai.19-2-2021.168725

中图分类号：

学科分类号：

摘要：

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) © 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.

引用

页码：1 / 7

页数：6

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