  In business domains, \textit{bundling} is one of the most important marketing
strategies to conduct product promotions, which is commonly used in online
e-commerce and offline retailers. Existing recommender systems mostly focus on
recommending individual items that users may be interested in. In this paper,
we target at a practical but less explored recommendation problem named bundle
recommendation, which aims to offer a combination of items to users. To tackle
this specific recommendation problem in the context of the \emph{virtual mall}
in online games, we formalize it as a link prediction problem on a
user-item-bundle tripartite graph constructed from the historical interactions,
and solve it with a neural network model that can learn directly on the
graph-structure data. Extensive experiments on three public datasets and one
industrial game dataset demonstrate the effectiveness of the proposed method.
Further, the bundle recommendation model has been deployed in production for
more than one year in a popular online game developed by Netease Games, and the
launch of the model yields more than 60\% improvement on conversion rate of
bundles, and a relative improvement of more than 15\% on gross merchandise
volume (GMV).
