Proceedings of the International Conference on Advancements in Materials, Design and Manufacturing for Sustainable Development, ICAMDMS 2024, 23-24 February 2024, Coimbatore, Tamil Nadu, India

Research Article

Enhanced Feature-Based Mobile Recommendations Through Hybrid Model

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  • @INPROCEEDINGS{10.4108/eai.23-2-2024.2346984,
        author={Likhitha  A and Jahnavi Lalasa  C and Aishwarya  K and Sandosh  S},
        title={Enhanced Feature-Based Mobile Recommendations Through Hybrid Model},
        proceedings={Proceedings of the International Conference on Advancements in Materials, Design and Manufacturing for Sustainable Development, ICAMDMS 2024, 23-24 February 2024, Coimbatore, Tamil Nadu, India},
        publisher={EAI},
        proceedings_a={ICAMDMS},
        year={2024},
        month={6},
        keywords={recommender system user-item collaborative filtering content-based filtering hybrid model},
        doi={10.4108/eai.23-2-2024.2346984}
    }
    
  • Likhitha A
    Jahnavi Lalasa C
    Aishwarya K
    Sandosh S
    Year: 2024
    Enhanced Feature-Based Mobile Recommendations Through Hybrid Model
    ICAMDMS
    EAI
    DOI: 10.4108/eai.23-2-2024.2346984
Likhitha A1,*, Jahnavi Lalasa C1, Aishwarya K1, Sandosh S1
  • 1: Vellore Institute of Technology
*Contact email: allaboina.likhitha2020@vitstudent.ac.in

Abstract

In today's world, smart devices have become an integral part of our daily routine, from the moment we wake up to the time we go to bed. Among these devices, smartphones stand out as a beneficial aid in making our lives easier. The project focuses on developing a recommendation system that can assist users in selecting a phone that best suits their specific requirements. To achieve this, a survey was conducted among students of a university to gain insights into their preferences. Which are analyzed using collaborative, content, and hybrid models. A Hybrid Recommendation System (HRS) is crucial in creating personalized content suggestions. It integrates user preferences and item attributes and uses collaborative and content-based models to generate accurate recommendations. HRS combines diverse data sources to improve the preciseness and relevance of the recommendations, making it a robust approach for personalized content suggestion systems.