Movies Recommender System – NLP (Semantic Similarity)

Semantic similarity is useful for building recommender systems or detecting plagiarism.

Texts can have similar structure, show similar ideas or discuss similar topics. Semantic similarity can be used for identifying target words or similarity between sentences or short texts.

Netflix uses Semantic Similarity identifying similar tastes and preferences using features like: Genre, categories, actors, release year.

I have used a movies.txt file that contains descriptions of movies to group similar type of movies as part of a recommendation engine system.

I have used short text descriptions of movies to recommend similar movies using the SpaCy library in python.

The function takes in the description of the movie and returns a title of a similar movie using semantic similarity (NLP) using description information as a key feature.

In this scenario, Planet Hulk was passed through and a similar movie returned (genre) Movie F; based on semantic similarity of text descriptions.

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