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Backend
Renders to the landing page of the web-app
Render to the search page of the web-app
Returns movie recommendations on the basis of user-input movies
Returns top-10 movie searches for an input string in the search box
Handles user feedback submission
Handles user feedback submission and mails the results
Renders to the success page
Utility function to create colored tags for different movie genres
Input: Movie genres;
Output: Colored tags for those genres
Utility function to beautify the feedback json containing predicted movies for sending in email
Input: Data obtained from frontend in json format;
Output: Beautified data dictionary containing movies grouped by watchlist category
Utility function for creating a dictionary for movie-genres mapping
Input: Data frame of movies.csv;
Output: Dictionary of movies-genres mapping
Utility function to send movie recommendations to user over email
Input : email of recipient_email and output of beautify_feedback_data;
Output: Sends email for valid email, otherwise raises exception in the server logs
Class that handles the search feature of the landing page.
Function to check movie prefix
Input : word/initial character(s);
Output : List of movies having that prefix
Function to check visited words
Input : Word and visited words;
Output : Words that have not been visited
Function to serve the result render
Input : A word/initial character(s);
Output : All titles starting with the given prompt.
Function to get top 10 results
Input : A word/initial character(s);
Output : Top 10 titles starting with the given prompt (taken from results)
Recommends movies to a user based on their past preferences and the preferences of users with similar tastes. Item-Item Collaborative Filtering (CF) is used to recommend similar movies based on user input. For example, if Joseph enjoyed Seven and Shutter Island, PopcornPicks might suggest The Prestige and Inception.
Generates a list of recommended movie titles for a new user based on their selections via item-item based CF.