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In e-commerce websites like Taobao, brand is playing a more important role ininfluencing users' decision of click/purchase, partly because users are nowattaching more importance to the quality of products and brand is an indicatorof quality. However, existing ranking systems are not specifically designed tosatisfy this kind of demand. Some design tricks may partially alleviate thisproblem, but still cannot provide satisfactory results or may create additionalinteraction cost. In this paper, we design the first brand-level ranking systemto address this problem. The key challenge of this system is how tosufficiently exploit users' rich behavior in e-commerce websites to rank thebrands. In our solution, we firstly conduct the feature engineeringspecifically tailored for the personalized brand ranking problem and then rankthe brands by an adapted Attention-GRU model containing three importantmodifications. Note that our proposed modifications can also apply to manyother machine learning models on various tasks. We conduct a series ofexperiments to evaluate the effectiveness of our proposed ranking model andtest the response to the brand-level ranking system from real users on alarge-scale e-commerce platform, i.e. Taobao.

A Brand-level Ranking System with the Customized Attention-GRU Model