A content-based recommender system suggests items to users based on the characteristics or features of the items themselves. In the case of a movie recommender system, it would recommend movies similar to a user's preferences by considering the content or features of the movies the user has liked.
The details of the movies(posters) are fetched using an API by TMDB,https://www.themoviedb.org/documentation/api, and using the IMDB id of the movie in the API.
Cosine similarity is a metric used to measure how similar the documents are irrespective of their size. Mathematically, it measures the cosine of the angle between two vectors projected in a multi-dimensional space. The cosine similarity is advantageous because even if the two similar documents are far apart by the Euclidean distance (due to the size of the document), chances are they may still be oriented closer together. The smaller the angle, higher the cosine similarity.
More about Cosine Similarity : Understanding the Math behind Cosine Similarity
Software And Tools Requirements
Technology and tools wise this project covers,
- Python
- Numpy and Pandas for data cleaning
- Data visualization
- Sklearn for model building
- Google Colab Notebook
