Analysis and Research on Increased Probability Matrix Factorization Techniques in Collaborative Filtering-Reference-Cited by-同舟云学术

Analysis and Research on Increased Probability Matrix Factorization Techniques in Collaborative Filtering

Published:2020-08-01 Issue: Volume: Page:182-187
ISSN:2394-4099
Container-title:International Journal of Scientific Research in Science, Engineering and Technology
language:en
Short-container-title:IJSRSET

Author:

Mounika Kongari¹,Suresh B. V. N. Krishna²

Affiliation:

1. M. Tech Scholar Department of CSE, NRI Institute of Technology Visadala, Guntur(Dt), Andhra Pradesh, India

2. Assistant Professor Department of CSE, NRI Institute of Technology Visadala, Guntur(Dt), Andhra Pradesh, India

Abstract

The matrix factorization algorithms such as the matrix factorization technique (MF), singular value decomposition (SVD) and the probability matrix factorization (PMF) and so on, are summarized and compared. Based on the above research work, a kind of improved probability matrix factorization algorithm called MPMF is proposed in this paper. MPMF determines the optimal value of dimension D of both the user feature vector and the item feature vector through experiments. The complexity of the algorithm scales linearly with the number of observations, which can be applied to massive data and has very good scalability. Experimental results show that MPMF can not only achieve higher recommendation accuracy, but also improve the efficiency of the algorithm in sparse and unbalanced data sets compared with other related algorithms.

Publisher

Technoscience Academy

Subject

General Medicine

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