Aayush Singh
Guru Gobind Singh Indraprastha University, New Delhi, Delhi 110032, India
*Authors to whom correspondence should be addressed.
Received: 2022-8-17 / Accepted: 2022-9-27 / Published: 2022-10-10
DOI: https://doi.org/10.37906/isteamc.2022.3
Abstract In this rising new era, the world is growing faster and so are we. People nowadays need effortless services, and recommendation system is one of them. They are present and used everywhere, whether it's advertising, e-commerce or any other domain. Companies such as Netflix, YouTube, Amazon, Spotify rely on recommendation systems to boost their sales. Moreover, it increases the engagement of users with a particular app or website as it tends to show more relevant individualized content. It basically takes the user’s past history as an input and then using filtering techniques it predicts the most suitable item/content for the user as an output. The main objective of this paper is to predict movies most desired by the user with as much accuracy as possible. In this paper, I will provide a brief description of various filtering methods and the particular techniques that I used to achieve the desired result. I have used Tf-Idf to quantify the overview of the movies, then did a pairwise similarity for the Content Based Filtering. This allows the user to watch movies based on their watch history according to the plot of the movies. For Collaborative Filtering, I have used Matrix Factorization to generate real-time recommendations based on user-movie rating matrix. Correlation Coefficient is used to calculate the score. The higher the score, the more likely the movie is liked by the user. I have used the voting system for Hybrid Filtering. Weighted Rating Score is calculated with the help of the votes given to the movies by the users. Then a popularity feature is also used to generate more meaningful movie recommendations. MinMax Scalar is used to normalize the data, and then fifty percent priority is given to both Weighted Rating Score and popularity and a final score is calculated. Using this final score, popular or top movies are generated. The databases used are retrieved from MovieLens and Kaggle. Consequently, my proposed system will generate recommendations based on the plot of the movie using tf-idf and cosine similarity, based on user-movie rating matrix and correlation coefficient, based on voting and popularity basis.
Research Areas: Artificial Intelligence
Keywords: Recommendation System, Content Based Filtering, Collaborative Filtering, Hybrid Filtering, Term Frequency-Inverse Document Frequency(Tf-Idf), Singular Value Decomposition, Weighted Rating Score
