  Recommender systems usually face the problem of serving the same
recommendations across multiple sessions regardless of whether the user is
interested in them or not, thereby reducing their effectiveness. To add
freshness to the recommended products, we introduce a feedback loop where the
set of recommended products in the current session depend on the user's
interaction with the previously recommended sets. We also describe ways of
addressing freshness when there is little or even no direct user interaction.
We define a metric to quantify freshness by reducing the problem to measuring
temporal diversity.
