Movie recommendation system using TF-IDF vectorization and Cosine Similarity

  PV. Snigdha,  M. Naveen,  S.Rahul

The internet has widened the horizons of numerous areas to engage and share relevant information in recent years. As it is said, everything has its advantages and disadvantages, thus with the increase in the field comes data saturation and data extraction difficulties. The suggestion system is critical in overcoming this challenge. Its purpose is to improve the user's experience by providing quick and comprehensible suggestions. Because of its ability to provide improved amusement, a movie suggestion is vital in our personal interaction. Users might be recommended a collection of movies depending on their interests or the appeal of the films. A recommendation system is being used to make suggestions for things to buy or see. They comb through a big database of information to lead people to the things that can suit their demands. A recommender system, also known as a recommendation engine or platform, is a type of data filtering system that attempts to forecast a user's "rating" or "preference" for an item. They're mostly employed for business purposes. This project outlines a content-based movie recommendation model which uses TF-IDF vectorization and cosine similarity for providing users with generic options based on film popularity and/or theme.

Keywords- Movie recommendation, Content-based recommendation system, TF-IDF vectorization, Cosine similarity, Data saturation, Data extraction

Unique Identification Number - IJEDR2203001
Page Number(s) - 1-8
Pubished in - Volume 10 | Issue 3 | July 2022
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  PV. Snigdha,  M. Naveen,  S.Rahul,   "Movie recommendation system using TF-IDF vectorization and Cosine Similarity", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.10, Issue 3, pp.1-8, July 2022, Available at :http://www.ijedr.org/papers/IJEDR2203001.pdf