Movie Recommender System using Content Based Filtering
Our movie recommendation engine works by suggesting movies to the user based on the metadata information. The similarity between the movies is calculated and then used to make recommendations. For that, our text data should be preprocessed and converted into a vectorizer using the CountVectorizer. As the name suggests, CountVectorizer counts the frequency of each word and outputs a 2D vector containing frequencies.
We don’t take into account the words like a, an, the (these are called “stopwords”) because these words are usually present in higher amounts in the text and don’t make any sense.
There exist several similarity score functions such as cosine similarity, Pearson correlation coefficient, etc. Here, we use the cosine similarity score as this is just the dot product of the vector output by the CountVectorizer.