• Prudhvi Shankar Aradhyula
• Suhasini T S
• Komal Ramapragada
• Pruthvi Teja G
• Aparna Laxmi A

Recommender System is a tool helping users find content and overcome information overload. It predicts interests of users and makes recommendation according to the interest of users. The original content-based movie recommender system is the continuation and development of collaborative filtering, which doesn’t need the user’s evaluation for items. Instead, the similarity is calculated based on the information of items that are chose by users, and then make the recommendation accordingly. Using natural language processing and vectorization techniques, it is possible to extract features from movie attributes like overview, genre, keywords, cast and crew to find movies with similar features. In this thesis, a feature extraction method is presented and the use of the extracted features in finding similar movies is investigated. We do the text pre-processing on a collection of movie attributes. We then extract tags from the collection using simple preprocessing techniques and store those tags to vectorize and then take out the similarity for each movie and recommend top-n items to the user.

Recommender systems, content-based filtering, similiarity.

"Exploring movie recommendations using content analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.f611-f622, June-2024, Available :http://www.jetir.org/papers/JETIR2406567.pdf

"Exploring movie recommendations using content analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppf611-f622, June-2024, Available at : http://www.jetir.org/papers/JETIR2406567.pdf