Open AccessMovie Recommendation System using Cosine Similarity and KNN
Author(s) -
Ramni Harbir Singh,
Sargam Maurya,
Tanisha Tripathi,
Tushar Narula,
Gaurav Srivastav
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.e9666.069520
Subject(s) - recommender system , computer science , cosine similarity , information overload , similarity (geometry) , domain (mathematical analysis) , information retrieval , popularity , the internet , world wide web , artificial intelligence , pattern recognition (psychology) , mathematics , psychology , mathematical analysis , social psychology , image (mathematics)
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.