Publication Type:

Conference Paper

Source:

2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Jaipur, India (2016)

ISBN:

9781509020294

URL:

https://ieeexplore.ieee.org/document/7732225

Keywords:

Clustering, Clustering algorithms, Engines, Informatics, Information filtering, information filtering mechanism, information overload, Internet, kNN, learning (artificial intelligence), Motion pictures, multilevel strategies, online shopping sites, pattern clustering, Prediction algorithms, product recommendation, Random forest, rating-based recommender system, Recommendation, Recommender System, Recommender systems, retail data processing, RMSE, root mean square error, sales promotion, Softmax Regression, SVD, Training, users preference

Abstract:

Recommender system has emerged as an integral part of the online shopping sites as it promotes sales. It recommends intuitive products based on users preference which solves the issue of information overload. Recommender system constitutes information filtering mechanism which filters vast amount of data. Algorithms such as SVD, KNN, Softmax Regression has already been used in the past to form recommendations. In this paper we propose a system which uses clustering and random forest as multilevel strategies to predict recommendations based on users ratings while targeting users mind-set and current trends. The result has been evaluated with the help of RMSE (Root Mean Square Error). Feasible performance has been achieved.

Cite this Research Publication

A. Ajesh, Jayashree Nair, and Jijin, P. S., “A Random Forest Approach for Rating-based Recommender System”, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, 2016.