Processing math: 100%

Movie Recommendation Project - Team 10 4641

Introduction (Motivation)

  • As streaming platforms like Netflix or YouTube have a lot of content, and a lot of information about users, there is an opportunity to provide users with content tailored to their interests.
  • In this project we aim to survey some of the collaborative methods available for generating recommendations, and evaluate which is most effective.

Method

  • Within recommendation systems, there are two types: Content Based and Collaborative Filtering. We are doing the later
  • Collaborative Filtering makes recommendations by utilizing what other users have watched
    • We only need to look at reviews dataset as it contains what all users have watched before.
    • The methods we look at include both unsupervised: (Matrix Factorization, SVD, K-Nearest Neighbors) and even supervised techniques with Neural Network (utilizing Keras and FastAI)
    • We use common evaluation techniques including Root Mean Square Error, Mean Absolute Error, and Explained Variance. We also look at training and testing time.
    • We use Cross Validation and Grid Search as well to aid with our techniques

Dataset

  • Movielens 100k (reviews dataset)
    • User id: unique identifier of the user that made the rating. (non-negative integer)
    • Item id: unique identifier of the movie the user rated. (non-negative integer)
    • Rating: number from 1-5 representing the opinion of the user of a certain movie (non-negative integer)
    • Timestamp: unix seconds representing the time the rating was made. (non-negative integer)

Movie Recommendation Project - Team 10 4641 Introduction (Motivation) As streaming platforms like Netflix or YouTube have a lot of content, and a lot of information about users, there is an opportunity to provide users with content tailored to their interests. In this project we aim to survey some of the collaborative methods available for generating recommendations, and evaluate which is most effective. Method Within recommendation systems, there are two types: Content Based and Collaborative Filtering. We are doing the later Collaborative Filtering makes recommendations by utilizing what other users have watched We only need to look at reviews dataset as it contains what all users have watched before. The methods we look at include both unsupervised: (Matrix Factorization, SVD, K-Nearest Neighbors) and even supervised techniques with Neural Network (utilizing Keras and FastAI) We use common evaluation techniques including Root Mean Square Error, Mean Absolute Error, and Explained Variance. We also look at training and testing time. We use Cross Validation and Grid Search as well to aid with our techniques Dataset Movielens 100k (reviews dataset) User id: unique identifier of the user that made the rating. (non-negative integer) Item id: unique identifier of the movie the user rated. (non-negative integer) Rating: number from 1-5 representing the opinion of the user of a certain movie (non-negative integer) Timestamp: unix seconds representing the time the rating was made. (non-negative integer)