How recommendation systems work in production?

0
votes

I am newbie to this and have started learning Spark. I have general question regarding how recommendation systems work in production environments or rather how is it deployed to production.

Below is a small example of a system for e-commerce website.

First Run of the engine

I understand that once the system is built, at the start we can feed the data to the engine(we can run the jobs or run the program/process for the engine) and it will give the results, which will be stored back to the database against each user. Next time when the user logins the website can fetch the data, previously computed data by the engine from the database and show as recommended items.

The confusion I have is how ‘these systems’, on the fly generate outputs based on the user activity. e.g. If I view a video on Youtube and I refresh the page, Youtube starts showing me similar videos. So, do we have these recommendation engine running always in background and they keep updating the results based on the user’s activity? How it is done so fast and quick?

2
apache-sparkmachine-learningrecommendation-engine
When you get to a resolution, please remember to up-vote useful things and accept your favourite answer (even if you have to write it yourself), so Stack Overflow can properly archive the question. - Prune

2 Answers

5
votes

Short answer:

  1. Inference is fast; training is the slow part.
  2. Retraining is a tweak to the existing model, not from scratch.
  3. Retraining is only periodic, rarely "on demand".

Long answer:

They generate this output based on a long baseline of user activity. This is not something the engine derives from your recent view: anyone viewing that video on the same day will get the same recommendations.

Some recommender systems will take your personal history into account, but this is usually just a matter of sorting the recommended list with respect to the predicted ratings for you personally. This is done with a generic model from your own ratings by genre and other characteristics of each video.

In general, updates are done once a day, and the changes are small. They don't retrain a model from scratch every time you make a request; the parameters and weights of your personal model are stored under your account. When you add a rating (not just viewing a video), your account gets flagged, and the model will be updated at the next convenient opportunity.

This update will begin with your current model, and will need to run for only a couple of epochs -- unless you've rated a significant number of videos with ratings that depart significantly from the previous predictions.

2
votes

To add to Prune's answer, depending on the design of the system it may also be possible to take into account the user's most recent interactions.

There are two main ways of doing so:

  • Fold-in: your most recent interactions are used to recompute the parameters of your personal model while leaving the remainder of the model unchanged. This is usually quite fast and could be done in response to every request.
  • A model that takes interactions as input directly: some models can compute recommendations directly by taking a user's interactions as input, without storing user representations that can only be updated via retraining. For example, you can represent the user by simply averaging the representations of the items she most recently interacted with. Recomputing this representation can be done in response to every request.

In fact, this last approach seems to form part of the YouTube system. You can find details in the Covington et al. Deep Neural Networks For YouTube Recommendations paper.