But, first, let us understand it mathematically. This blog will use cosine similarity as a similarity metric to implement the Spotify Playlist recommender. The higher the score, the more the probability of the user finding that song of his/her interest. Then the model must score each candidate according to this similarity metric, and then the model will recommend according to this score. First of all, we will have to pick a similarity metric, like cosine similarity. Now, this model should recommend songs that users may find interesting. Some user-related features will be provided explicitly by the user, and other features will be implicit based on the characteristic information.
The user will also be represented in the same feature space. Let’s say features of the song include genre, and the matrix is binary such that the non-zero value represents that the song has that feature present. The figure below shows a feature matrix where each row represents a song, and each column represents a feature. To explain it further, we will be taking an example of a simple Spotify song recommender. Types of Recommendation SystemsĬontent-Based Filtering systems use characteristic information that recommends new items/products to a user based on their past actions or explicit feedback. User-item interactions: Information that defines user-item relationship (rating, like/dislike, etc.).īased on this, we can categorize two broad classes of the algorithm used in a Recommendation System.Characteristic information: Information that defines the profile of a product (tag, category, etc.) or a user (preferences, profile, etc.).The function of a Recommendation System mainly depends on two kinds of information: These systems can do so because of user data. Introduction What is a Recommendation System?Ī Recommendation System aims to predict the user’s choices and recommend the product or service that is likely to be interesting. Implementing a Spotify playlist Recommender Engine from scratch using Python.Mathematics behind Recommendation Systems.Key takeawaysīy the end of this blog, you will know the following: Spotipy is a lightweight Python library that we will be using to access the Spotify Web API. Readers are also requested to go through the documentation of the Spotipy library. The reader should have basic knowledge of Python libraries like pandas, numpy, scikit-learn, and the basics of vector algebra.