In some cases, it is also essential to consider the freshness of the recommendation. This consideration is especially important for articles or posts on forums. Fresh entries should often get to the top. The correction factors (damping factors) are usually used to make such updates. The following formulas are used for calculating the rating of articles on media sites.

Here is an example of a rating calculation in the Hacker news magazine:

Here, U denotes upvotes, D denotes downvotes, P denotes penalty (additional adjustment for the implementation of other business rules), and T denotes recording time.

The following equation shows a Reddit rating calculation:

Here, U denotes the number of upvotes, D denotes the number of downvotes, and T denotes the recording time. The first term evaluates the quality of the record, and the second makes a correction for the time.

There is no universal formula, and each service invents the formula that best solves its problem; it can be tested only empirically.

The following section will discuss the existing approaches to testing recommender systems. This is not a straightforward task because it's usually hard to estimate the quality of a recommendation without exact target values in a training dataset.