Implementation of Popular Techniques for Movie Recommendations
G. Naga Sujini1, D. Gyana Deepika2
1G. Naga Sujini, Assistant Professor, Department of Computer Science & Engineering, Mahatma Gandhi Institute of Technology, affiliated to JNTU-H. Hyderabad, India.
2D. Gyana Deepika, Department of Computer Science Engineering from Mahatma Gandhi Institute of Technology affiliated to JNTU-H., Hyderabad, India.
Manuscript received on March 07, 2020. | Revised Manuscript received on March 16, 2020. | Manuscript published on March 30, 2020. | PP: 3311-3315 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8602038620/2020©BEIESP | DOI: 10.35940/ijrte.F8602.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In today’s world where there is a plethora of movies to choose from in any medium, recommendation systems play a crucial role in reducing the search time for the movie watchers by recommending them the most relevant movies that they would most probably like. There are a wide range of approaches and techniques which are used for recommending movies. While some techniques reflect the current trend and popularity of movies, others are able to capture and analyze the past behavior of the viewer and make recommendations accordingly. Recommendation systems are an integral part of companies such as Netflix, Amazon Prime, Hulu and various others to ensure that their customers have a pleasant experience which in turn would boost the company’s profits. This study discusses and analyses the various approaches and techniques used for the recommendation of movies.
Keywords: Collaborative Filtering, Content Filtering, Recommendation Systems
Scope of the Article: Collaborative Applications.