With the release of TensorRec v0.21, I’ve added the ability to easily use deep neural networks in your recommender system.
For some recommender problems, such as cold-start recommendation problems, deep learning can be an elegant solution for learning from user and item metadata. Using TensorRec with Keras, you can now experiment with deep representation models in your recommender systems quickly and easily.
In a TensorRec model, the components that learn how to process user and item features are called the “representation graphs” (or “repr” for short). These graphs convert high-dimensional user/item features, such as metadata and indicator variables, into low-dimensional user/item representations.
Define your model’s representation graphs as a sequence of Keras layers by extending the class
AbstractKerasRepresentationGraph. Next, overwrite the
create_layers abstract method to return an ordered list of Keras layers.
With this dataset, the recommender system is learning to recommend books to users based on user metadata (location, age) and book metadata (author, title, year publisher, etc). This example compares four different configurations of a recommender system using combinations of linear kernel representations and deep network representations.
The architecture of this network has not been optimized for the Book Crossing problem, and should be regarded only as an example of the new Keras functionality.
A Word of Caution
When you’re holding a hammer (or deep learning), everything looks like a nail (or a deep learning problem).
Many real-world recommendation problems are best solved through thoughtful analysis, feature engineering, simple models, and effective feedback systems. Deep models are highly flexible, but this makes them particularly susceptible to overfitting through overparameterization. In these cases, performance with previously-unknown users and items can be significantly harmed.
An example of this issue is the third configuration in the Book Crossing example above. This system is the most effective of the four configurations at making recommendations for existing users based only on their age and location but, based on the same user metadata, is the worst performer on new users — a classic example of overfitting. This problem could be avoided with richer metadata, better feature engineering, or simpler models.
The Book Crossing example also illustrates that the best product outcomes may be from having multiple recommender systems. A product that is driven by two recommender systems, one designed and optimized for warm-start users and another for new cold-start users, may be the optimal system design for your product.