Open AccessA User- Based Recommendation with a Scalable Machine Learning Tool
Author(s) -
Ch. Veena,
B. Vijaya Babu
Publication year - 2015
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v5i5.pp1153-1157
Subject(s) - computer science , recommender system , scalability , collaborative filtering , construct (python library) , set (abstract data type) , machine learning , frame (networking) , selection (genetic algorithm) , world wide web , information retrieval , artificial intelligence , data mining , database , programming language , telecommunications
Recommender Systems have proven to be valuable way for online users to recommend information items like books, videos, songs etc.colloborative filtering methods are used to make all predictions from historical data. In this paper we introduce Apache mahout which is an open source and provides a rich set of components to construct a customized recommender system from a selection of machine learning algorithms.[12] This paper also focuses on addressing the challenges in collaborative filtering like scalability and data sparsity. To deal with scalability problems, we go with a distributed frame work like hadoop. We then present a customized user based recommender system.