  As a new type of e-commerce platform developed in recent years, local
consumer service platform provides users with software to consume service to
the nearby store or to the home, such as Groupon and Koubei. Different from
other common e-commerce platforms, the behavior of users on the local consumer
service platform is closely related to their real-time local context
information. Therefore, building a context-aware user behavior prediction
system is able to provide both merchants and users better service in local
consumer service platforms. However, most of the previous work just treats the
contextual information as an ordinary feature into the prediction model to
obtain the prediction list under a specific context, which ignores the fact
that the interest of a user in different contexts is often significantly
different. Hence, in this paper, we propose a context-aware heterogeneous graph
attention network (CHGAT) to dynamically generate the representation of the
user and to estimate the probability for future behavior. Specifically, we
first construct the meta-path based heterogeneous graphs with the historical
behaviors from multiple sources and comprehend heterogeneous vertices in the
graph with a novel unified knowledge representing approach. Next, a multi-level
attention mechanism is introduced for context-aware aggregation with graph
vertices, which contains the vertex-level attention network and the path-level
attention network. Both of them aim to capture the semantic correlation between
information contained in the graph and the outside real-time contextual
information in the search system. Then the model proposed in this paper
aggregates specific graphs with their corresponding context features and
obtains the representation of user interest under a specific context and input
it into the prediction network to finally obtain the predicted probability of
user behavior.
