  In e-commerce websites like Taobao, brand is playing a more important role in
influencing users' decision of click/purchase, partly because users are now
attaching more importance to the quality of products and brand is an indicator
of quality. However, existing ranking systems are not specifically designed to
satisfy this kind of demand. Some design tricks may partially alleviate this
problem, but still cannot provide satisfactory results or may create additional
interaction cost. In this paper, we design the first brand-level ranking system
to address this problem. The key challenge of this system is how to
sufficiently exploit users' rich behavior in e-commerce websites to rank the
brands. In our solution, we firstly conduct the feature engineering
specifically tailored for the personalized brand ranking problem and then rank
the brands by an adapted Attention-GRU model containing three important
modifications. Note that our proposed modifications can also apply to many
other machine learning models on various tasks. We conduct a series of
experiments to evaluate the effectiveness of our proposed ranking model and
test the response to the brand-level ranking system from real users on a
large-scale e-commerce platform, i.e. Taobao.
