  Users consume their favorite content in temporal proximity of consumption
bundles according to their preferences and tastes. Thus, the underlying
attributes of items implicitly match user preferences, however, current
recommender systems largely ignore this fundamental driver in identifying
matching items. In this work, we introduce a novel temporal proximity filtering
method to enable items-matching. First, we demonstrate that proximity
preferences exist. Second, we present an induced similarity metric in temporal
proximity driven by user tastes and third, we show that this induced similarity
can be used to learn items pairwise similarity in attribute space. The proposed
model does not rely on any knowledge outside users' consumption bundles and
provide a novel way to devise user preferences and tastes driven novel items
recommender.
