Assignment

Now that we have covered basic techniques for recommender systems, choose one commercial recommender and describe how you think it works (content-based, collaborative filtering, etc). Does the technique deliver a good experience or are the recommendations off-target?

You may also choose one of the three non-personalized recommenders (below) we went over in class and describe the technique and which of the three you prefer to use.

Metacritic: How We Create the Metascore Magic

Rotten Tomatoes: About Rotten Tomatoes

IMDB: FAQ for IMDb Ratings

IMDB Recommender System

IMDB uses recommendation systems by which system tries to find the links between contents through observing users patterns of watching films and reviews : - content-based recommendation systems which suggests movies as per “People who liked this also liked”. - tag-based systems where IMDb can provide certain tags (genres of movies), and users can tag content themselves. Netflix model latent factor model is an example.

Recommendation can be performed in these ways: Identification of movies rated by predefined appreciated threshold. Identification of unwatched movies similar to the appreciated ones.

Challenges:

  1. There needs to be enough other users already in the system to find a match.
  2. If there are many items to be recommended, even if there are many users, the user/ratings matrix is sparse, and it is hard to find users that have rated the same items.

Possible Approach:

We could use other metrics based on the interaction between users and content. Number of likes, number of comments and watch time. Action lablels by click through=1, like=2, comment=3 and share=4, this allows us to calculate the similarity to determine how strong a tie is between users