Stokastik

Machine Learning, AI and Programming

Tag: Matrix Factorization

Factorization Machines for Movie Recommendations

In the last series of posts we have looked at how to recommend movies to users based on the historical ratings. The two most promising approaches were Collaborative Filtering and Matrix Factorization. Both these approaches learns the user-movie preferences only from the ratings matrix. Recall that in the first post of the series, we had started with an approach known as the Content Based Recommendation, where we created a regression […]

Continue Reading →

Designing Movie Recommendation Engines - Part III

In the last two parts of this series, we have been looking at how to design and implement a movie recommendations engine using the MovieLens' 20 million ratings dataset. We have looked at some of the most common and standard techniques out there namely Content based recommendations, Collaborative Filtering and Latent Factor based Matrix Factorization strategy. Clearly CF and MF approaches emerged as the winners due to their accuracy and […]

Continue Reading →

Designing Movie Recommendation Engines - Part II

In the last post, we had started to design a movie recommendation engine using the 20 million ratings dataset available from MovieLens. We started with a Content Based Recommendation approach, where we built a classification/regression model for each user based on the tags and genres assigned to each movie he has rated. The assumption behind this approach is that, the rating that an user has given to a movie depends […]

Continue Reading →