DOIONLINE 
DOIONLINE Publish In |
International Journal of Advances in Electronics and Computer Science-IJAECS |
 Journal Home Volume Issue |
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Issue |
Volume-9,Issue-9 ( Sep, 2022 ) | |||||||||
Paper Title |
A Hybrid Top-N Movie Recommendation System | |||||||||
Author Name |
Nakshatra Jagtap, Siddhivinayak Kulkarni | |||||||||
Affilition |
School of Computer Science and Engineering,Dr. Vishwanath Karad MIT World Peace University,Pune-411038, India | |||||||||
Pages |
1-5 | |||||||||
Abstract |
Abstract - In a broad sense, recommender systems are algorithms that propose suitable products to consumers based on their preferences. Depending on the industry, these products can range from a film to buying items on e-commerce sites.Recommender systems include playlist spinners for music and video applications, product recommenders for online enterprises, content recommender systems for social media sites, and public web-based material recommenders. In some businesses, recommender systems are crucial because they can earn a large amount of revenue or serve as a method to distinguish oneself from competitors. The rapid increase in the volume of digital content offered and the volume of Internet consumers has caused a possible problem of data overload, preventing quick access to interesting products on the internet these days. Recommendation systems address the issue of data saturation. Users like to be offered things they might like, and when they use a service that completely matches their preferences, they are more likely to return to that service.In recommendation system, collaborative filtering and content-based filtering, as well as other methods like knowledge-based structures, are often utilized.The cold start problem is a regular occurrence in the recommender system. This problem is related to making suggestions for new users or items. When it comes to new users, the system doesn't even have enough information to make recommendations since it doesn't recognize their interests. If the item is completely new, there is no record of who purchased, streamed, or evaluated it. This challenge could be solved using a hybrid recommendation system [12]. We studied different research articles on recommendation system using a hybrid approach and constructed a hybrid model that suggests a varied catalogue of movies to users in this study. Keywords - Recommendation Systems, Collaborative Filtering, Content-Based Filtering, Recommendations, Hybrid Recommendation Systems. | |||||||||
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