Movie Recommendation System Using Content Based Filtering

Abstract

— Several advanced level platforms, such as Information Gathering, Learning Techniques, the Internet - Of - things (IoT), and Deep Learning, have emerged as a result of technological breakthroughs. We use technology almost everywhere we operate to meet social demands. In addition, new systems have been developed as a result of this. In recent times, recommendation engines have risen in importance, whether it be in entertainment, education, or other businesses. Previously, users had to decide which publications to buy, which films to watch, and which songs to listen to, among other things. A content-based algorithm's cornerstones are material collection and quantitative analysis. As the study of text acquiring and filtering has progressed, many modern content-based recommendation engines now offer recommendations based on text information analysis. This paper discusses the content-based recommender. The film has several characteristics that set it apart from other recommender systems, including diversity and uniqueness. These features are used to build a movie prototype and determine similarity. We present a novel method for calculating feature weights that improves movie representation. Finally, we examine the strategy to determine how it has progressed.