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Content Based Movie Recommendation System
K. Meghana1,E. Sudeekasha2 , A. Somanth3 , Dr. Y. Srinivasulu4
1, 2, 3Student, 4Professor, Department of Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Ghatkesar, Hyderabad, India.
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Abstract: Recommender System is a tool which helps users find the required content and overcome information overload. It predicts interests of users by using Machine Learning algorithms and makes recommendation according to the interest of users. The primary content-based recommender system is the continuation and development of collaborative filtering, which does not need the user’s appraisal for items. Instead, the similarity is calculated based on the data of items that are selected by users, and then make the recommendation appropriately. With the augmentation of machine learning, the current content-based recommender system can build profile for users and products respectively. Building or renewing the profile according to the perusal of items that are bought or seen by users. The system can differentiate the user and the profile of items and then recommend the most resembling products. So, this recommender method that compel user and product directly can’t be brought into collaborative filtering model. The groundwork of content-based algorithm is acquisition and quantitative analysis of the content. The research of acquisition and filtering of text information are fully fledged, many current modified content-based recommender systems make recommendations according to the analysis of text data. This paper introduces content-based recommendation system for the movie websites. There are a lot of factors extracted from the movie, they are diverse and unique, which is also different from other recommender systems. We use these aspects to construct movie model and calculate similarity. We introduce a new outlook for setting weight of features, which improvises the representation of movie recommendations. Finally, we evaluate the approach to illustrate the improvement.
Keywords: Recommendation system, content-based filtering, collaborative filtering, similarity, movie
I. INTRODUCTION
Everyone loves movies regardless of age, gender, race, skin color or geographic location. We are all connected in some way through this amazing medium. But what's most interesting is how unique our choices and combinations are when it comes to movie tastes. Some people like movies of a certain genre, such as thrillers, romances, sci-fi, while others focus on starring and directing. All patterns of behavior, not just from the audience, but from the film itself. The recommender system is a simple algorithm whose goal is to provide users with the most relevant information by discovering patterns in a data set. Algorithms rank the items and show users the items they rate highly. An example of a recommendation in action is when you go to Amazon and see that some products are recommended for you, or Netflix recommends a particular movie for you. It's also used by music streaming apps like Spotify and Deezer to recommend music you listen and movies to your liking as well.
Two users buy the same product A and B in an e-commerce store. When this happens, a similarity index is calculated for these two users of hers. Depending on the score, the system can recommend item C to other users. These two users of hers are perceived as similar in terms of the items they are purchasing.
ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538
Volume 11 Issue I Jan 2023- Available at www.ijraset.com
II. OBJECTIVE
The purpose of content-based filtering is to categorize products by specific keywords, know customer preferences search databases for those terms, and recommend similar ones
III. LITERATUREREVIEW
After research was done to recommend items from fixed databases, two major recommendation techniques emerged: content-based and collaborative. Content-based recommendation recommends articles that are similar to the user, while collaborative recommendation identifies users with similar tastes and recommends articles they like. Later, with the development of recommender systems, hybrid methods were invented, combining two or more methods. Before the invention of recommendation systems, you had to read reviews and choose the movie that best suited your interests or choose a movie at random based on other criteria. The rapid increase in the number of movies available online made this difficult.
