This engine uses a Consine Similarity and Eucledean Distance to recommend a book to a selected book by the user
Its a filtering system that uses above algorithms to filter through large datasets
Recommendations done using content-based recommenders can be seen as a user-specific classification problem. This classifier learns the user's likes and dislikes from the features of the song. The most straightforward approach is keyword matching. In a few words, the idea behind is to extract meaningful keywords present in a song description a user likes, search for the keywords in other song descriptions to estimate similarities among them, and based on that, recommend those songs to the user.
In our case, because we are working with text and words, Term Frequency-Inverse Document Frequency (TF-IDF) can be used for this matching process.
The mongodb path containing the dataset must contain title and genre for cosine similarity.
Dataset used in the example Kaggle goodreads
- After cloning, run npm install to download and install all the required dependencies.
- Run node index.js to start the server at localhost:3000/