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I create an application with some social interaction between users. Users will have ratings that determines your recommendations - users are recommended to users with the same rating.Total rating of user is the sum of the partial ratings multiplied by their coefficients of significance.

$totalRating = \sum_{i=0} partialRating_i*coefficient_i$

But only the total rating is used in the final for recommendations.

Each partial rating has a coefficient of significance and a decreasing time interval.

Examples of partial ratings:

  • online status. When the user logs into the application. this rating rise to 100, after 5 hours(decreasing time interval) of inactivity it decreases to 75, after 12 hours of inactivity - 50, etc.
  • boost for beginners. When the user creates his profile, this rating is equals to 100 and decreases every 2 days by 25 points, until it drops to zero.
  • activity in application. The more a user interacts with other users, the greater his rating. As with other ratings, it drops to zero after a certain period of inactivity.

So, the main headache - how to calculate the most suitable coefficients of significance and decreasing time intervals?

I understand that these parameters will be selected rather based on my preferences. But is there any method that can somehow help choose these parameters?

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