  Preferential attachment models were shown to be very effective in predicting
such important properties of real-world networks as the power-law degree
distribution, small diameter, etc. Many different models are based on the idea
of preferential attachment: LCD, Buckley-Osthus, Holme-Kim, fitness, random
Apollonian network, and many others.
  Although preferential attachment models reflect some important properties of
real-world networks, they do not allow to model the so-called recency property.
Recency property reflects the fact that in many real networks vertices tend to
connect to other vertices of similar age. This fact motivated us to introduce a
new class of models - recency-based models. This class is a generalization of
fitness models, which were suggested by Bianconi and Barabasi. Bianconi and
Barabasi extended preferential attachment models with pages' inherent quality
or fitness of vertices. When a new vertex is added to the graph, it is joined
to some already existing vertices that are chosen with probabilities
proportional to the product of their fitness and incoming degree.
  We generalize fitness models by adding a recency factor to the attractiveness
function. This means that pages are gaining incoming links according to their
attractiveness, which is determined by the incoming degree of the page (current
popularity), its inherent quality (some page-specific constant) and age (new
pages are gaining new links more rapidly).
  We analyze different properties of recency-based models. In particular, we
show that some distributions of inherent quality lead to the power-law degree
distribution.
