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.
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 state-of-the-art performance of the proposed model.
Understanding and predicting latent emotions of users toward online contents, known as social emotion mining, has become increasingly important to both social platforms and businesses alike. Despite recent developments, however, very little attention has been made to the issues of nuance, subjectivity, and bias of social emotions. In this paper, we fill this gap by formulating social emotion mining as a robust label ranking problem, and propose: (1) a robust measure, named as G-mean-rank (GMR), which sets a formal criterion consistent with practical intuition; and (2) a simple yet effective label ranking model, named as ROAR, that is more robust toward unbalanced datasets (which are common). Through comprehensive empirical validation using 4 real datasets and 16 benchmark semi-synthetic label ranking datasets, and a case study, we demonstrate the superiorities of our proposals over 2 popular label ranking measures and 6 competing label ranking algorithms. The datasets and implementations used in the empirical validation are available for access.
Label ranking aims to map instances to an order over a predefined set of labels. It is ideal that the label ranking model is trained by directly maximizing performance measures on training data. However, existing studies on label ranking models mainly based on the minimization of classification errors or rank losses. To fill in this gap in label ranking, in this paper a novel label ranking model is learned by minimizing a loss function directly defined on the performance measures. The proposed algorithm, referred to as BoostLR, employs a boosting framework and utilizes the rank aggregation technique to construct weak label rankers. Experimental results reveal the initial success of BoostLR.
Object ranking or "learning to rank" is an important problem in the realm of preference learning. On the basis of training data in the form of a set of rankings of objects represented as feature vectors, the goal is to learn a ranking function that predicts a linear order of any new set of objects. In this paper, we propose a new approach to object ranking based on principles of analogical reasoning. More specifically, our inference pattern is formalized in terms of so-called analogical proportions and can be summarized as follows: Given objects $A,B,C,D$, if object $A$ is known to be preferred to $B$, and $C$ relates to $D$ as $A$ relates to $B$, then $C$ is (supposedly) preferred to $D$. Our method applies this pattern as a main building block and combines it with ideas and techniques from instance-based learning and rank aggregation. Based on first experimental results for data sets from various domains (sports, education, tourism, etc.), we conclude that our approach is highly competitive. It appears to be specifically interesting in situations in which the objects are coming from different subdomains, and which hence require a kind of knowledge transfer.