Recommender system using MovieLens dataset.

I've used two MovieLens datasets.

The Full Dataset: Consists of 26,000,000 ratings and 750,000 tag applications applied to 45,000 movies by 270,000 users. Includes tag genome data with 12 million relevance scores across 1,100 tags.

The Small Dataset: Comprises of 100,000 ratings and 1,300 tag applications applied to 9,000 movies by 700 users.

First I build a simple recommender system.

Install requirements:

sudo pip3 install requirements.txt

The Simple Recommender offers generalized recommendations to every user based on movie popularity and (sometimes) genre. The basic idea behind this recommender is that movies that are more popular and more critically acclaimed will have a higher probability of being liked by the average audience. This model does not give personalized recommendations based on the user.

The implementation of this model is extremely trivial. All we have to do is sort our movies based on ratings and popularity and display the top movies of our list. As an added step, we can pass in a genre argument to get the top movies of a particular genre.

I use the TMDB Ratings to come up with our Top Movies Chart. I will use IMDB's weighted rating formula to construct my chart. Mathematically, it is represented as follows:

Weighted Rating (WR) = (vv+m.R)+(mv+m.C)

where,

v is the number of votes for the movie
m is the minimum votes required to be listed in the chart
R is the average rating of the movie
C is the mean vote across the whole report

The next step is to determine an appropriate value for m, the minimum votes required to be listed in the chart. I use 95th percentile as the cutoff. In other words, for a movie to feature in the charts, it must have more votes than at least 95% of the movies in the list.

I have built the overall Top 250 Chart and have defined a function to build charts for a particular genre.

To qualify to be considered for the chart, a movie has to have at least 434 votes on TMDB. We also see that the average rating for a movie on TMDB is 5.244 on a scale of 10. 2274 Movies qualify to be on the chart.

We see that three Christopher Nolan Films, Inception, The Dark Knight and Interstellar occur at the very top of our chart. The chart also indicates a strong bias of TMDB Users towards particular genres and directors.

I construct a function that builds charts for particular genres. For this, I relax on the default conditions to the 85th percentile instead of 95.