  Recommender systems are a valuable way to engage users in a system, increase
participation and show them resources they may not have found otherwise. One
significant challenge is that user interests may change over time and certain
items have an inherently temporal aspect. As a result, a recommender system
should try and take into account the time-dependant user-item relationships.
However, temporal aspects of a user profile may not always be explicitly
available and so we may need to infer this information from available
resources. Product reviews on sites, such as Amazon, represent a valuable data
source to understand why someone bought an item and potentially who the item is
for. This information can then be used to construct a dynamic user profile. In
this paper, we demonstrate utilising reviews to extract temporal information to
infer the \textit{age category preference} of users, and leverage this feature
to generate time-dependent recommendations. Given the predictable and yet
shifting nature of age and time, we show that, recommendations generated using
this dynamic aspect lead to higher accuracy compared with techniques from state
of art. Mining temporally related content in reviews can enable the recommender
to go beyond finding similar items or users to potentially predict a future
need of a user.
