Non-Linear Label Ranking for Large-Scale Prediction of Long-Term User Interests


  • Nemanja Djuric Yahoo! Labs
  • Mihajlo Grbovic Yahoo! Labs
  • Vladan Radosavljevic Yahoo! Labs
  • Narayan Bhamidipati Yahoo! Labs
  • Slobodan Vucetic Temple University



computational advertising, ad targeting, label ranking, large-scale learning, non-linear models


We consider the problem of personalization of online services from the viewpoint of 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's revenue. We propose to address this problem as a task of ranking the ad categories depending on a user's preference, and introduce a novel label ranking approach capable of efficiently learning non-linear, highly accurate models in large-scale settings. Experiments on 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 benefit of using the proposed model on large-scale ranking problems.




How to Cite

Djuric, N., Grbovic, M., Radosavljevic, V., Bhamidipati, N., & Vucetic, S. (2014). Non-Linear Label Ranking for Large-Scale Prediction of Long-Term User Interests. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).



Main Track: Novel Machine Learning Algorithms