Switching hybrid model for personalized recommendations by combining users demographic information

RAGHAVENDRA C K; SRIKANTAIAH K.C

No Detail Found!

Switching hybrid model for personalized recommendations by combining users demographic information

Author(s)

RAGHAVENDRA C K Computer Science & Engineering, SJB Institute of Technology, Bangalore, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka 590018, India; Computer Science & Engineering, BNM Institute of Technology, Bangalore, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka 590018, India

SRIKANTAIAH K.C Computer Science & Engineering, BNM Institute of Technology, Bangalore, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka 590018, India

Abstract

Recommendation systems are smart tools that are necessary to help users to find relevant items and for ECommerce platforms to improve their revenues. List of items based on its rating and content are searched using collaborative filtering (CF) and content-based filtering (CBF). The recommendation system mainly depends on quality of item recommended and the rating of items given by the existing users. In this work a switching based hybrid model which is the combination of collaborative, content and demographic based model was built for recommending books. Cold start problem was solved using demographic data of users. Experimental analysis has been made on individual and hybrid models using RMSE and MAE metrics. Results show that hybrid model outperforms all the other traditional models in terms of predictions and quality of recommendations.

Publication Details

Page(s) 825-835

DOI DOI not available

Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 3, Year: 2022

Request the full text from PASTIC

Keywords

Collaborative Filtering Technique, Demographic Based Recommender System, Book Recommendation System, Content Based Filtering, Hybrid Filtering Technique, Cold Start

Citations

Downloads

Views