![]() This cache has a ttl limit for a day, thus the data will get updated daily. Then I get that similarity matrix and final list from Kaggle api in the streamlit app and store it in cache. I scheduled the notebook daily so it can send updated data daily. I save that similarity matrix and final processed movie list in kaggle after pickling them. Then I find cosine similarity between those movies' ids in a matrix. Then based on that movie id I transform those relevant tags into a vector on the basis of the frequency using countvectorizer from Scikit-Learn. and merge them using their movies' imdb ids. Then I get all relevent tags such as top actors' name, directors, writers, genres etc. How I built itĪt first I gathered data (Movies name, ratings, casts, crew) from IMDB Website which gets updated daily. When enter search for a movie in this web application, it shows the details of the movie and it recommends few other movies and their details based on that search. ![]() So I decided to build a basic content based recommender system web app. Various sources say that as much as 35–40% of tech giants’ revenue comes from recommendations alone. This often results in increased revenue for the platform itself. The more relevant products a user finds on the platform, the higher their engagement. Recommender systems help to personalize a platform and help the user find something they like. Think of the examples above: streaming videos, social networking, online shopping the list goes on. For any given product, there are sometimes thousands of options to choose from. We now live in what some call the “era of abundance”.
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