  What people buy is an important aspect or view of lifestyles. Studying
people's shopping patterns in different urban regions can not only provide
valuable information for various commercial opportunities, but also enable a
better understanding about urban infrastructure and urban lifestyle. In this
paper, we aim to predict city-wide shopping patterns. This is a challenging
task due to the sparsity of the available data -- over 60% of the city regions
are unknown for their shopping records. To address this problem, we incorporate
another important view of human lifestyles, namely mobility patterns. With
information on "where people go", we infer "what people buy". Moreover, to
model the relations between regions, we exploit spatial interactions in our
method. To that end, Collective Matrix Factorization (CMF) with an interaction
regularization model is applied to fuse the data from multiple views or
sources. Our experimental results have shown that our model outperforms the
baseline methods on two standard metrics. Our prediction results on multiple
shopping patterns reveal the divergent demands in different urban regions, and
thus reflect key functional characteristics of a city. Furthermore, we are able
to extract the connection between the two views of lifestyles, and achieve a
better or novel understanding of urban lifestyles.
