• Omkar Sawant
• Swami Samant
• Manav Shah
• John Kenny

Today, there is a sizably voluminous variety of different approaches and algorithms for data filtering and recommendations giving. Online content and accommodation providers deal with the quandary of providing “relevant” content on a customary substructure, especially due to the sheer volume of data available. This work deals with one such quandary, namely, that of soothsaying utilizer predilection for movies utilizing the Netflix database. We present a recollection-predicated Collaborative Filtering (CF) algorithm that learns the personality traits of the users in a features space we call the Latent Genre Space (LGS). This representation sanctions us to utilize traditional clustering algorithms in this space, and overcome one of the most sizably voluminous quandaries in these works – that of different lengths of utilizer feature vectors in the voting space. Inference techniques in this space are discussed, and a KD-tree predicated most proximate-neighbor scheme is implemented. In the terminus, we will show the main challenges recommender systems come across.

collaborative filtering, content-based filtering, database, most proximate-neighbor, recommendation.

"Movie Recommendation System ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.c601-c607, April-2022, Available :http://www.jetir.org/papers/JETIR2204285.pdf

"Movie Recommendation System ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 4, page no. ppc601-c607, April-2022, Available at : http://www.jetir.org/papers/JETIR2204285.pdf