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Improving the Accuracy of Recommender Systems Through Annealing

  • Shefali Arora
  • Shivani Goel
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 10)

Abstract

Matrix factorization is a scalable approach used in recommender systems. It deals with the problem of sparse matrix ratings in datasets. The learning rate parameter in matrix factorization is obtained by using numerical methods like stochastic gradient descent. Learning rate affects the accuracy of the system. In this paper, we make use of annealing schedules which will impact the value of learning rate. Five annealing schedules namely exponential annealing, inverse scaling, logarithmic cooling, linear multiplicative cooling and quadratic multiplicative cooling have been used to affect the learning rate and thus the accuracy of our recommender system. The experimental results on Movielens (http://www.grouplens.org/datasets/movielens) dataset with different sizes show that minimum mean absolute error for the system is obtained by exponential annealing at a lower value of learning rate and by linear multiplicative cooling at higher learning rate values. Apache Mahout (http://www.mahout/apache.org) 0.9 is chosen as the platform for conducting the experiments.

Keywords

Recommender system Matrix factorization Annealing Mahout 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringThapar UniversityPatialaIndia

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