What is an item matrix?
In recommendation systems, a matrix of embedding vectors generated by matrix factorization that holds latent signals about each item.
item matrix explained in plain English
In recommendation systems, a matrix of embedding vectors generated by matrix factorization that holds latent signals about each item. Each row of the item matrix holds the value of a single latent feature for all items. For example, consider a movie recommendation system. Each column in the item matrix represents a single movie. The latent signals might represent genres, or might be harder-to-interpret signals that involve complex interactions among genre, stars, movie age, or other factors. The item matrix has the same number of columns as the target matrix that is being factorized. For example, given a movie recommendation system that evaluates 10,000 movie titles, the item matrix will have 10,000 columns.
Example
Practitioners refer to item matrix when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
People also read
- bag of words
A representation of the words in a phrase or passage, irrespective of order.
- bidirectional language model
A language model that determines the probability that a given token is present at a given location in an excerpt of text based on the preceding and following text.
- cross-entropy
A generalization of Log Loss to multi-class classification problems.
- dimension reduction
Decreasing the number of dimensions used to represent a particular feature in a feature vector, typically by converting to an embedding vector.
- dimensions
Overloaded term having any of the following definitions: The number of levels of coordinates in a Tensor.
- distillation
The process of reducing the size of one model (known as the teacher) into a smaller model (known as the student) that emulates the original model's predictions as faithfully as possible.
- embedding layer
A special hidden layer that trains on a high-dimensional categorical feature to gradually learn a lower dimension embedding vector.
- embedding space
The d-dimensional vector space that features from a higher-dimensional vector space are mapped to.
- embedding vector
Broadly speaking, an array of floating-point numbers taken from any hidden layer that describe the inputs to that hidden layer.
- encoder
In general, any ML system that converts from a raw, sparse, or external representation into a more processed, denser, or more internal representation.