  Combining graph representation learning with multi-view data (side
information) for recommendation is a trend in industry. Most existing methods
can be categorized as \emph{multi-view representation fusion}; they first build
one graph and then integrate multi-view data into a single compact
representation for each node in the graph. However, these methods are raising
concerns in both engineering and algorithm aspects: 1) multi-view data are
abundant and informative in industry and may exceed the capacity of one single
vector, and 2) inductive bias may be introduced as multi-view data are often
from different distributions. In this paper, we use a \emph{multi-view
representation alignment} approach to address this issue. Particularly, we
propose a multi-task multi-view graph representation learning framework (M2GRL)
to learn node representations from multi-view graphs for web-scale recommender
systems. M2GRL constructs one graph for each single-view data, learns multiple
separate representations from multiple graphs, and performs alignment to model
cross-view relations. M2GRL chooses a multi-task learning paradigm to learn
intra-view representations and cross-view relations jointly. Besides, M2GRL
applies homoscedastic uncertainty to adaptively tune the loss weights of tasks
during training. We deploy M2GRL at Taobao and train it on 57 billion examples.
According to offline metrics and online A/B tests, M2GRL significantly
outperforms other state-of-the-art algorithms. Further exploration on diversity
recommendation in Taobao shows the effectiveness of utilizing multiple
representations produced by \method{}, which we argue is a promising direction
for various industrial recommendation tasks of different focus.
