2 research outputs found
Non-linear Label Ranking for Large-scale Prediction of Long-Term User Interests
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
advertisers' 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 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 benefit of using the proposed
model on large-scale ranking problems.Comment: 28th AAAI Conference on Artificial Intelligence (AAAI-14
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Multi-Prototype Label Ranking with Novel Pairwise-to-Total-Rank Aggregation
We propose a multi-prototype-based algorithm for online learning of soft pairwise-preferences over labels. The algorithm learns soft label preferences via minimization of the proposed soft rank-loss measure, and can learn from total orders as well as from various types of partial orders. The soft pairwise preference algorithm outputs are further aggregated to produce a total label ranking prediction using a novel aggregation algorithm that outperforms existing aggregation solutions. Experiments on synthetic and real-world data demonstrate stateof-the-art performance of the proposed model.