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Non-linear Label Ranking for Large-scale Prediction of Long-Term User Interests

Publication
Jul 27, 2014
Abstract

We consider the problem of personalization of online services from the viewpoint of display ad targeting, where we seek to find the best ad categories to be shown to each user, resulting in improved user experience and increased advertiser revenue. We propose to reformulate this problem as a label ranking task, and introduce a novel label ranking approach, capable of efficiently learning non-linear, highly accurate models in large-scale settings. Experiments on a real-world advertising data set with more than 3.2 million users show that the proposed algorithm outperforms the existing solutions in terms of both rank loss and top-K retrieval performance, strongly suggesting the benefits of the proposed ranking model in a web-scale setting of targeted advertising.

  • AAAI Conference on Artificial Intelligence (AAAI-14)
  • Conference/Workshop Paper

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