Hybrid and Ensemble-Based Personalized Recommender System - Solving Data Sparsity Problem
3rd IEEE International Conference on Transdisciplinary AI, TransAI 2021
; : 116-121, 2021.
Article
in English
| Scopus | ID: covidwho-1752450
ABSTRACT
Online content streaming is the most popular form of entertainment in recent times due to COVID 19 lockdown. All popular streaming services use various product recommendation schemes to retain users to their services by intriguing them with content that they might like. Various recommendation systems have been used by famous streaming services like Netflix, Amazon Prime, Hulu, etc. but they lack consistency and accuracy as they suffer from some severe problems such as the first rater problem, sparsity problem, and various computations problems. In this research, we have come up with a hybrid machine learning recommender system which uses an ensemble of content-based and collaborative filtering techniques to not only solve all data sparsity problems but also provide more personalized recommendations to the users based on their watching history and user profile. This research provides a new algorithm that increases the quality of content that is being recommended to the users. © 2021 IEEE.
Data sparsity; First rater problem; Hybrid approach; Online content streaming; Personalized recommendations; Recommender systems; Collaborative filtering; Online systems; User profile; Data sparsity problems; Online content; Personalized recommendation; Personalized recommender systems; Streaming service; System solving
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
3rd IEEE International Conference on Transdisciplinary AI, TransAI 2021
Year:
2021
Document Type:
Article
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