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Full Length Article
Fusion: Practice and Applications
Volume 6 , Issue 1, PP: 32-42 , 2021 | Cite this article as | XML |PDF

Title

A Personalized Recommender System

Authors Names :   Akshit Nassa   1 *     Shubham Gupta   2     Pranjal Jindalm   3     Achin Jain   4     P. Singh Lamba   5  

1  Affiliation :  Bharati Vidyapeeth’s College of Engineering, INDIA

    Email :  akshitnassa412@gmail.com


2  Affiliation :  Bharati Vidyapeeth’s College of Engineering, INDIA

    Email :  shubham.gupta1704@gmail.com


3  Affiliation :  Bharati Vidyapeeth’s College of Engineering, INDIA

    Email :  pranjaljindalpj@gmail.com


4  Affiliation :  Bharati Vidyapeeth’s College of Engineering, INDIA

    Email :  achin.mails@gmail.com


5  Affiliation :  Bharati Vidyapeeth’s College of Engineering, INDIA

    Email :  singhs.puneet@gmail.com



Doi   :   https://doi.org/10.54216/FPA.060104

Received: March 12, 2021 Accepted: August 23, 2021

Abstract :

Due to social media, e-commerce, and the broader digitization of businesses, a data surge has occurred during the previous decade. The information is used to make informed decisions, forecast market trends, and identify patterns in consumer preferences. Following the widespread adoption of internet services, recommendation systems have become commonplace. The idea is to use filtering algorithms to recommend products to users who might be interested in them. Users are given recommendations for a media item such as movies by discovering user profiles of people who share similar interests. The preferences of users are first determined by allowing them to rate movies of their choosing. After some time, the recommender system will be able to better understand the user and recommend films that are more likely to get higher ratings. It also considers the impact of personal and situational factors on the user experience. In comparison to previous models, the experimental findings on the TMDB dataset provide a dependable model that is precise and generates more customized movie recommendations.

Keywords :

 

Recommender system; Movie recommendation; filtering techniques; Dataset; Personalization;       

User Experience

 

 

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Cite this Article as :
Akshit Nassa , Shubham Gupta , Pranjal Jindalm , Achin Jain , P. Singh Lamba, A Personalized Recommender System, Fusion: Practice and Applications, Vol. 6 , No. 1 , (2021) : 32-42 (Doi   :  https://doi.org/10.54216/FPA.060104)