Movie Recommendation System using Cosine Similarity and KNN 
Ramni Harbir Singh1, Sargam Maurya2, Tanisha Tripathi3, Tushar Narula4, Gaurav Srivastav5
1Gaurav Srivastav*, Inderprastha Engineering College, AKTU.
2Ramni Harbir Singh, Inderprastha Engineering College, AKTU
3Sargam Maurya, Inderprastha Engineering College, AKTU.
4Tanisha Tripathi, Inderprastha Engineering College, AKTU.
5Tushar Narula, Inderprastha Engineering College, AKTU
Manuscript received on May 25, 2020. | Revised Manuscript received on June 05, 2020. | Manuscript published on June 30, 2020. | PP: 556-559 | Volume-9 Issue-5, June 2020. | Retrieval Number: E9666069520/2020©BEIESP | DOI: 10.35940/ijeat.E9666.069520
Open Access | Ethics and Policies | Cite | Mendeley
© 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: Over the past years, the internet has broadened the horizon of various domains to interact and share meaningful information. As it is said that everything has its pros and cons therefore, along with the expansion of domain comes information overload and difficulty in extraction of data. To overcome this problem the recommendation system plays a vital role. It is used to enhance the user experience by giving fast and coherent suggestions. This paper describes an approach which offers generalized recommendations to every user, based on movie popularity and/or genre. Content-Based Recommender System is implemented using various deep learning approaches. This paper also gives an insight into problems which are faced in content-based recommendation system and we have made an effort to rectify them.
Keywords: Recommendation System, Content-Based Recommender System, Deep learning