Movie Recommendation engine
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Movie Recommendation engine

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We have built an online Movie Recommender System which is based on the analysis of users' ratings history to several movies and their demographic information. We used data from Movielens website. ...

We have built an online Movie Recommender System which is based on the analysis of users' ratings history to several movies and their demographic information. We used data from Movielens website. Collaborative filtering and matrix factorization techniques have been used for the implementation. The end result is a web application where a user is recommended with top 20 movies.
Codebase: http://goo.gl/nM7RMy
Demo Video: http://goo.gl/VgZ2uI

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Movie Recommendation engine Presentation Transcript

  • 1. An Online Social Network based Recommendation System Team: Jayesh Lahori Nikhar Agarwal Sharad Gupta Vedavathi Mannepalli
  • 2. ● We have built an online Movie Recommender System which is based on the analysis of users' ratings history to several movies and their demographic information. ● We used data from Movielens website. ● Collaborative filtering and matrix factorization techniques have been used for the implementation. ● The end result is a web application where a user is recommended with top 20 movies. Abstract
  • 3. ● We have built a Movie Recommender system using Movielens dataset. ● We are provided with User's ratings to some of the available movies Movies information , Demographic information about the users. ● Using the above information and applying collaborative filtering and matrix factorization techniques, top 20 movies have been recommended to the users. Introduction
  • 4. We first used cosine similarity method to find similar users and later recommended movies by applying several heuristics (using age, movie genre, gender, occupation etc). This method even though gave accurate results, its performance deteriorated when user-movie ratings matrix became sparse. Hence we came up with a better approach which uses matrix factorization technique to predict the ratings. This technique can used to meet the real time scenario where the utility matrix is often sparse. Introduction (contd..)
  • 5. The two approaches used will be mentioned in detail in coming slides. We have also developed a module which suggests movies for the facebook users based on the movies he liked and also from the movies liked by people in his friends list. Suggestion is based on the movie genres. Approach
  • 6. Approach A: Using Collaborative filtering and Demographic Information ● In this approach, top movies are recommended to users by finding out the similar users using cosine distance similarity and demographic information of users, and then applying several heuristics. ● Such an approach shows explain-ability of the results but its performance decreases when matrix data gets sparse.
  • 7. Flow Chart for Approach A
  • 8. ● Our second approach uses matrix-factorization method of collaborative Filtering for the rate prediction and ranking. ● SVDFeature has been used to implement the same. SVDFeature is a machine learning toolkit for feature-based collaborative filtering. ● The feature-based setting allows us to build factorization models. ● SVDFeature will learn a feature-based matrix factorization model with the given training data and make predictions on supplied test feature files. Approach B: Using Matrix Factorization for Collaborative Filtering
  • 9. Flow Chart for Approach B
  • 10. Evaluation (Using Matrix Factorization) Evaluation can be done based on RMSE value after every model is generated. Ideally RMSE value should be zero (0) Two chunks of disjoint data has been taken from the dataset. One for training and the other for testing. The training was done for 40 rounds. For the first round RMSE value came out to be 1.265039. Eventually it became better and for the 40th round it is 0.932842.
  • 11. Conclusion Even though Approach A's advantages include; the explainability of the results, which is an important aspect of recommendation systems and new data can be added easily, the disadvantage identified is its performance decreases when data gets sparse, which is frequent with web related items. This prevents the scalability and has problems with large datasets. This can be overcome by Matrix Factorization Method.It handles the sparsity better than the previous one. This helps with scalability with large data sets. It improves the prediction performance. All at the cost of expensive model building.