Movie-Recommendation-App

It is a movie recommender App which recommends you movie according to your interest and ratings, I used Content and popularity based filtering which generates movie recommendation using Machine Learning python script running in cloud pushing all the processed results to the user mobile application.

UI sample

Login	Registration	Recommendations

Rating Bar

credit - https://github.com/sujithkanna/SmileyRating

Firebase Database

Recommender Engine

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Basic functioning of the engine

order to build the recommendation engine, I will basically proceed in two steps:

• 1/ determine $N$ films with a content similar to the entry provided by the user
• 2/ select the 5 most popular films among these $N$ films

Similarity

When builing the engine, the first step thus consists in defining a criteria that would tell us how close two films are. To do so, I start from the description of the film that was selected by the user: from it, I get the director name, the names of the actors and a few keywords. I then build a matrix where each row corresponds to a film of the database and where the columns correspond to the previous quantities (director + actors + keywords) plus the k genres.

In this matrix, the $a_{ij}$ coefficients take either the value 0 or 1 depending on the correspondance between the significance of column $j$ and the content of film $i$. For exemple, if "keyword 1" is in film $i$, we will have $a_{ij}$ = 1 and 0 otherwise. Once this matrix has been defined, we determine the distance between two films according to:

                                 d(m,n) = sqrt( sum( a(m,i) - a(n,i) ) ) 

At this stage, we just have to select the N films which are the closest from the entry selected by the user.

Popularity

According to similarities between entries, we get a list of $N$ films. At this stage, I select 5 films from this list and, to do so, I give a score for every entry. I decide de compute the score according to 3 criteria:

• the IMDB score
• the number of votes the entry received
• the year of release

The two first criteria will be a direct measure of the popularity of the various entries in IMDB. For the third criterium, I introduce the release year since the database spans films from the early $XX^{th}$ century up to now. I assume that people's favorite films will be most of the time from the same epoch.

Then, I calculate the score according to the formula: score = IMDB^2 * gaussian kernel(vote_count) * gaussian kernel(year of release)

gaussian function:

exp( - (x - mean)/(2*correlaton cofficient))

USE find_similarities(dataframe , index of movie , del_sequels = True, verbose = True) FUNCTION TO GET YOUR RECOMMENDATIONS.