A Novel Rule based Data Mining Approach towards Movie Recommender System

Author:

Sharma Mugdha1,Ahuja Laxmi1ORCID,Kumar Vinay2

Affiliation:

1. Amity School of Engineering &Technology, Amity University, Noida, India

2. Vivekananda Institute of Professional Studies, Guru Gobind Singh Indraprastha University, Delhi, India

Abstract

The proposed research work is an effort to provide accurate movie recommendations to a group of users with the help of a rule-based content-based group recommender system. The whole approach is categorized into 2 phases. In phase 1, a rule- based approach has been proposed which considers the users’ viewing history to provide the Rule Base for every individual user. In phase 2, a novel group recommendation system has been proposed which considers the ratings of the movies as per the rule base generated in phase 1. Phase 2 also considers the weightage of every individual member of the group to provide the accurate movie recommendation to that particular group of users. The results of experimental setup also establish the fact that the proposed system provides more accurate outcomes in terms of precision and recall over other rule learning algorithms such as C4.5.

Publisher

Faculty of Organisation and Informatics

Subject

Library and Information Sciences,Computer Science Applications,Information Systems

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1. Geometry Arithmetic Problem Recommendation Based on Scene-Enhanced BERT;2023 International Conference on Intelligent Education and Intelligent Research (IEIR);2023-11-05

2. Time-Bin-Based Neighbourhood Algorithm for Temporal Effects in Recommendation Systems;Tehnicki vjesnik - Technical Gazette;2022-12-15

3. An optimal context-aware content-based movie recommender system using genetic algorithm: a case study on MovieLens dataset;Journal of Experimental & Theoretical Artificial Intelligence;2022-12-06

4. Enhanced Vaccine Recommender System to prevent COVID-19 based on Clustering and Classification;2021 International Conference on Engineering and Emerging Technologies (ICEET);2021-10-27

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