Users bundle the consumption of their favorite content in temporal proximity to each other, 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 a temporal proximity induced similarity metric 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 and provide a novel way to devise user preferences and tastes driven novel items recommender.