Recommendation system for news articles? 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?
 A: I see it is not a matter of regularly training your model on a daily basis. It seems that what it is required is to firstly build a classifier for news article; business, weather, sport, technology (etc.). This is necessarily required unless it is applied already
For the time factor, stated in your question, you can add a feature to the model whether the classification of a news article is affected by this factor. Then, a recommender system can be built based on matching demographics of users with the views on news articles (already classified by the news classifier). 
In regards to the "dozens or hundreds of news articles published every day", this can be rich (and free) sources for data for your model, generally speaking, which it does not require you to train your model every day. That is, building a predictive model differs from building a descriptive model (whether for real-time analysis or not). By a predictive model, it can be predicted (or forecasted) what will happen in the future. Therefore, we train data recorded in the past to be used in predicting what may happen. However, such a model could require you update your model bi-weekly or monthly as a result of the huge volume of data increases every day.
