User Reposed Based Movie Recommendation System


  • Prashant Verma


Model-based, Memory-based, Content-based, Hybrid, Recommendation,Collaborative filtering


Filtering devices are often used to extract unwanted data from vast amounts of data. Recommender systems look for and predict useful and insightful things that a user might enter into the data. Filtering technologies are being used to exclude irrelevant data in vast amounts of information. The approach focuses on repurposing the knowledge and preferences of users in order to calculate future suggestions. This article presents a recommender system that generates suggestions depending on user data. It is achieved by examining the person's psychological analysis, watching history, and film reviews from many other websites. It's also predicated on requirements of aggregate resemblance. Both material and data aggregation are used in this approach. Both of these things may be described as follows: Collaboration filtration is the process of constructing structures going by past user activities. The system is then utilized to anticipate results that perhaps the customer would be interesting in (i.e. items that have already been selected or scored).People rely on their own events to make judgments that are in their economic interest, thus the suggestion system is commonplace. Recommendation algorithms are a sort of data filtration which anticipates customer interests for items they utilize or are considering purchasing. The real effectiveness of such systems is still being researched.




How to Cite

Prashant Verma. (2021). User Reposed Based Movie Recommendation System. Journal of Research Proceedings, 1(2), 138–147. Retrieved from