In the scenario where we recommend movies that a user has not yet watched based on how he/she rated previously watched movies, it seems that:
- A movie released 5 years ago is [possibly] as good a candidate for recommendation as a movie released 5 hours ago.
- The amount of movies released over the course of a week is typically rather small.
How could those two things be address in a recommender system in the context of news articles, where:
- An article published 5 days ago is likely irrelevant (too old).
- There are dozens or hundreds of new articles published every day.
For example, almost nobody cares to read an article about the weather two or three days ago.
How do you train a model (and keep it up to date) where the data has such a short usable live span?