  In recent years, much research effort on recommendation has been devoted to
mining user behaviors, i.e., collaborative filtering, along with the general
information which describes users or items, e.g., textual attributes,
categorical demographics, product images, and so on. Price, an important factor
in marketing --- which determines whether a user will make the final purchase
decision on an item --- surprisingly, has received relatively little scrutiny.
  In this work, we aim at developing an effective method to predict user
purchase intention with the focus on the price factor in recommender systems.
The main difficulties are two-fold: 1) the preference and sensitivity of a user
on item price are unknown, which are only implicitly reflected in the items
that the user has purchased, and 2) how the item price affects a user's
intention depends largely on the product category, that is, the perception and
affordability of a user on item price could vary significantly across
categories. Towards the first difficulty, we propose to model the transitive
relationship between user-to-item and item-to-price, taking the inspiration
from the recently developed Graph Convolution Networks (GCN). The key idea is
to propagate the influence of price on users with items as the bridge, so as to
make the learned user representations be price-aware. For the second
difficulty, we further integrate item categories into the propagation progress
and model the possible pairwise interactions for predicting user-item
interactions. We conduct extensive experiments on two real-world datasets,
demonstrating the effectiveness of our GCN-based method in learning the
price-aware preference of users. Further analysis reveals that modeling the
price awareness is particularly useful for predicting user preference on items
of unexplored categories.
