  Prerequisites can play a crucial role in users' decision-making yet
recommendation systems have not fully utilized such contextual background
knowledge. Traditional recommendation systems (RS) mostly enrich user-item
interactions where the context consists of static user profiles and item
descriptions, ignoring the contextual logic and constraints that underlie them.
For example, an RS may recommend an item on the condition that the user has
interacted with another item as its prerequisite. Modeling prerequisite context
from conceptual side information can overcome this weakness. We propose
Prerequisite Driven Recommendation (PDR), a generic context-aware framework
where prerequisite context is explicitly modeled to facilitate recommendation.
We first design a Prerequisite Knowledge Linking (PKL) algorithm, to curate
datasets facilitating PDR research. Employing it, we build a 75k+ high-quality
prerequisite concept dataset which spans three domains. We then contribute
PDRS, a neural instantiation of PDR. By jointly optimizing both the
prerequisite learning and recommendation tasks through multi-layer perceptrons,
we find PDRS consistently outperforms baseline models in all three domains, by
an average margin of 7.41%. Importantly, PDRS performs especially well in
cold-start scenarios with improvements of up to 17.65%.
