Research Article
A Hybrid Approach for Mobile Phone Recommendation using Content-Based and Collaborative Filtering
@ARTICLE{10.4108/eetiot.4594, author={B V Chandrahaas and Bhawani Sankar Panigrahi and Sagar Dhanraj Pande and Nirmal Keshari Swain}, title={A Hybrid Approach for Mobile Phone Recommendation using Content-Based and Collaborative Filtering}, journal={EAI Endorsed Transactions on Internet of Things}, volume={10}, number={1}, publisher={EAI}, journal_a={IOT}, year={2023}, month={12}, keywords={Mobile Phone Recommendation, Content-based filtering, Collaborative filtering, Machine Learning, Smart Phones, Hybrid Systems, Recommendation System, Personalized Recommendations, Mobile device industry, Smartphone selection, User preferences}, doi={10.4108/eetiot.4594} }- B V Chandrahaas
Bhawani Sankar Panigrahi
Sagar Dhanraj Pande
Nirmal Keshari Swain
Year: 2023
A Hybrid Approach for Mobile Phone Recommendation using Content-Based and Collaborative Filtering
IOT
EAI
DOI: 10.4108/eetiot.4594
Abstract
INTRODUCTION: The number of manufacturers and models accessible in the market has increased due to the growing trend of mobile phone use. Customers now have the difficult task of selecting a phone that both fits their demands and offers good value. Although recommendation algorithms already exist, they frequently overlook the various aspects that buyers take into account before making a phone purchase. Furthermore, recommendation systems are now widely used tools for using huge data and customising suggestions according to user preferences. OBJECTIVES: Machine learning techniques like content-based filtering and collaborative filtering have demonstrated promising outcomes among the different methodologies proposed for constructing these kinds of systems. A hybrid recommendation system that combines the benefits of collaborative filtering with content-based filtering is presented in this paper for mobile phone choosing. The suggested method intends to deliver more precise and customised recommendations by utilising user behaviour patterns and mobile phone content properties. METHODS: The system makes better recommendations by analysing user preferences and phone similarities using the aforementioned machine learning methods. The technology that has been built exhibits its capability to aid customers in selecting a mobile phone with knowledge. RESULTS: With the effective Hybridization process we have obtained the best possible scores of MSE, MAE and RMSE. CONCLUSION: To sum up, the growing intricacy of the mobile phone industry and the abundance of options have demanded the creation of increasingly advanced recommendation systems. This work presents a hybrid recommendation system that efficiently blends collaborative and content-based filtering techniques to provide users with more tailored, superior recommendations. This approach has the ability to enable customers to choose the best mobile phone for their needs by taking into account both user behaviour and mobile phone characteristics.
Copyright © 2023 B. V. Chandrahaas et al., licensed to EAI. This is an open access article distributed under the terms of the CC BYNC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
