We describe the 1st place winning approach for the CIKM Cup 2016 Challenge. In this paper, we provide an approach to reasonably identify same users across multiple devices based on browsing logs. Our approach regards a candidate ranking problem as pairwise classification and utilizes an unsupervised neural feature ensemble approach to learn latent features of users. Combined with traditional hand crafted features, each user pair feature is fed into a supervised classifier in order to perform pairwise classification. Lastly, we propose supervised and unsupervised inference techniques.
翻译：我们描述 CICKM 2016 杯挑战的第一胜点。 在本文中, 我们提供一种基于浏览日志的合理识别多个设备中相同用户的方法。 我们的方法将候选人排名问题视为对称分类, 并使用一种不受监督的神经特征共通方法来学习用户的潜在特征。 与传统的手工制作特征相结合, 每个用户配对特征被注入一个受监督的分类器, 以便进行对称分类 。 最后, 我们提出有监督且不受监督的推断技术 。
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