The Magic of Matrices in Machine Learning & Recommender Systems

Credit for the image goes to “Tivadar Danka”

Matrix representation and matrix multiplication play foundational roles in machine learning and recommender systems. The intuition behind their use can be best understood by breaking it down into a few key points:

1. Data Representation: Matrix Form: In machine learning, datasets are often represented in matrix form where each row is a data instance (e.g., a user) and each column represents a feature (e.g., movie rating, product preference). Vectors: In the context of natural language processing, for example, word embeddings can represent words as high-dimensional vectors. Matrices store these vectors for an entire vocabulary. Reference: Word Embeddings
2. Linear Transformations & Operations: Matrices are instrumental in performing linear transformations. Think of neural networks: each layer performs a transformation, often represented as matrix multiplication followed by an activation function. Matrix multiplication efficiently captures the interaction between features, which is crucial for capturing patterns in data. Reference: Neural Networks
3. Recommender Systems & Collaborative Filtering: In recommender systems, especially collaborative filtering, we have a user-item matrix. The entries of this matrix represent user interactions with items (e.g., ratings given by users to movies). The…