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A Comprehensive Analysis on Movie Recommendation System Employing Collaborative Filtering by Urvish Thakker; Ruhi Patel; Manan Shah is a Computer Science article available to read on EtoBox.

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Collaborative Filtering (CF) is one of the most extensively used technologies for Recommender Systems (RS), it shows an improved intelligent searching mechanism for recommending personalized items. It effectively makes use of the information retained by the application to find similarities between the sections of the application. Apart from RS, other applications of CF making use of the sensing and monitoring of data are environmental sensing, mineral study, financial services, marketing, and many more. Different industries like Tourism, Television, E-Learning, etc. make use of this technology, software such as Customer Relationship Management also make use of this technology. This paper discusses the prowess CF algorithm and its applications for Movie Recommendation System (MRS). It gives a brief overview of collaborative filtering consisting of two major approaches: user-based approach and Item-based approaches. Further, in model-based filtering methodology, it is discussed how machine learning algorithms can be implemented for movie recommendation purposes and also to predict the ratings of the unrated movies and bifurcate or sort movies as per the user preference. Followed by,

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Author

Urvish Thakker; Ruhi Patel; Manan Shah

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)