Hands-On Machine Learning with C++

Cold start problem

A cold start is a typical situation when a sufficient amount of data has not yet been accumulated for the correct operation of the recommender system (for example, when a product is new or is just rarely bought). If the ratings of only three users estimate the average rating, such an assessment is not reliable, and users understand this. In such situations, ratings are often artificially adjusted.

The first way to do this is to show not the average value, but the smoothed average (damped mean). With a small number of ratings, the displayed rating leans more to a specific safe average indicator, and as soon as a sufficient number of new ratings are typed, the averaging adjustment stops operating.

Another approach is to calculate ...