1. Top n recommendation system using selectable random and hybrid machine learning techniques
Authors : Abhijit D. Awari, Dr. Mahesh R. Dube
DOI : http://dx.doi.org/10.21172/1.111.11
Keywords : Recommendation System (RS)Machine LearningClassificationData PreprocessingKNNPCA Abstract :Recommendation systems(RS) attempt to recommend the most suitable item to users by using different predictive algorithms. Recommendation systems are used to perform three main tasks. These tasks include rating prediction in which RS aims to fill the missing entries in User-Item Rating Matrix, Top N recommendation in which system generates a ranked list of N items to users, Classification in which items are classified into correct categories. In this paper, we have proposed a generic model for a Top N Recommendation system using Selectable Random an Hybrid Machine Learning Techniques. We have implemented a data pre-processing module and a classification module using different machine learning techniques. The proposed system generates the Top N recommendations. We have used the Wine data set which is available on Internet. The proposed system can be tweaked to generate the recommendations according to the data set. Experimental results show that how our recommender system works on an example dataset. The system is evaluated using different evaluation metrics.
Citing this Journal Article :Abhijit D. Awari, Dr. Mahesh R. Dube, "Top n recommendation system using selectable random and hybrid machine learning techniques", Volume 11 Issue 1 - July 2018,
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