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Recommender systems are among the most popular applications of data science today. They are used to predict the "rating" or "preference" that a user would give to an item. Amazon uses recommendation system to suggest products to customers, YouTube uses it to decide which video to play next on autoplay, and Facebook uses it to recommend pages to like and people to follow.
Why: Problem statement
Most of the people were not aware of the good and popular movies. They waste their time on searching it.
How: Solution description
In this challenge, we are going to complete the analysis of Tamil movies in the year of 2017. In particular, we are going to recommend the movies by applying the tools of machine learning such as Simple recommender, Content-Based recommender, and Collaborative filtering.
I collected the list of Tamil movies data from some leading website. The dataset contains Title of the movie, Director of the movie, cast (actors who participated in various roles), Producer of the movie, the year that the movie released, ratings, and the vote counts.
Simple recommenders are basic systems that recommend the top items based on a certain metric or score. In this section, I built a simplified clone of top Tamil movies in the year of 2017.
The following are the steps involved:
Calculate the ratings for every movie.
Sort the movies based on the ratings and output the top results.
One of the most basic metrics is rating. For one, it does not take into consideration the popularity of a movie. Therefore, a movie with a rating of 9 from 10 voters will be considered 'better'.
As the number of voters increases, the rating of a movie regularizes and approaches towards a value that is reflective of the movie's quality. It is more difficult to discern the quality of a movie with extremely few voters.
Taking these shortcomings into consideration, it is necessary that we come up with a weighted rating that takes into account the average rating and the number of votes it has gathered. Such a system will make sure that a movie with a 9 rating from 1000 voters gets a far higher score.
The average rating of a movie on Tamil movie dataset is around 6.29, on a scale of 10. I calculated the average number of votes, received by each movie is 515.
I calculated the metric for each qualified movie. To do this, I define a function, weighted_rating() and define a new feature wr, of which I calculated the value by applying this function to the DataFrame of qualified movies. Finally, I sorted the DataFrame based on the wr feature and the output title, vote count, vote average and weighted rating or score of the top 10 movies.
I calculated the genre for each qualified movie. To do this, I defined a function, build_chart() for the genre, of which I calculated the value by applying this function to the DataFrame of qualified movies.
I built a system that recommends movies that are similar to a particular movie. More specifically, I computed pairwise similarity scores for all movies based on their plot descriptions and recommend movies based on that similarity weighted ratings. The plot description is available as Overview feature in the dataset.
In its current form, it is not possible to compute the similarity between any two overviews. To do that, I computed the word vectors of each overview, as it will be called from now on. I computed the Term Frequency-Inverse Document Frequency (TF-IDF) vectors for each overview. This will give a matrix where each column represents a word in the overview vocabulary (all the words that appear in at least one document) and each column represents a movie.
I analyzed that 1717 different words were used to describe the 191 movies in the dataset.
I used the cosine similarity to calculate a numeric quantity that denotes the similarity between the two movies. I used the cosine similarity score since it is independent of magnitude and is relatively easy and fast to calculate.
I defined a function that takes in a movie title as an input and outputs a list of the 10 most similar movies. I used reverse mapping of movie titles and DataFrame indices.
These systems are extremely similar to the content-based recommendation engine that I built. These systems identify similar items based on how people have rated it in the past. Here I took three columns and found the items that were similar in all the three columns named Director, Genre, and ratings.
By the nature of our system, it is not an easy task to evaluate the performance since there are no right or wrong recommendations. It is just a matter of opinions. This is the RMSE and MAE results between the top 5 movies.
Mean RMSE: 1.9462
Mean MAE: 1.6732
How is it different from competition
I collected the Tamil movie dataset whereas others don't.
Who are your customers
People who are curious about watching various types of movies can use this project. Data Scientists can use this for study and literature survey.