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Mihajlo Grbovic

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4 papers

1 author row

AAAI Conference 2014 Conference Paper

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

Nemanja Djuric
Mihajlo Grbovic
Vladan Radosavljevic
Narayan Bhamidipati
Slobodan Vucetic

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.

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IJCAI Conference 2013 Conference Paper

Multi-Prototype Label Ranking with Novel Pairwise-to-Total-Rank Aggregation

Mihajlo Grbovic
Nemanja Djuric
Slobodan Vucetic

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.

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AAAI Conference 2012 Conference Paper

Convex Kernelized Sorting

Nemanja Djuric
Mihajlo Grbovic
Slobodan Vucetic

Kernelized sorting is a method for aligning objects across two domains by considering within-domain similarity, without a need to specify a cross-domain similarity measure. In this paper we present the Convex Kernelized Sorting method where, unlike in the previous approaches, the cross-domain object matching is formulated as a convex optimization problem, leading to simpler optimization and global optimum solution. Our method outputs soft alignments between objects, which can be used to rank the best matches for each object, or to visualize the object matching and verify the correct choice of the kernel. It also allows for computing hard one-to-one alignments by solving the resulting Linear Assignment Problem. Experiments on a number of cross-domain matching tasks show the strength of the proposed method, which consistently achieves higher accuracy than the existing methods.

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AAAI Conference 2012 Conference Paper

Sparse Principal Component Analysis with Constraints

Mihajlo Grbovic
Christopher Dance
Slobodan Vucetic

The sparse principal component analysis is a variant of the classical principal component analysis, which finds linear combinations of a small number of features that maximize variance across data. In this paper we propose a methodology for adding two general types of feature grouping constraints into the original sparse PCA optimization procedure. We derive convex relaxations of the considered constraints, ensuring the convexity of the resulting optimization problem. Empirical evaluation on three real-world problems, one in process monitoring sensor networks and two in social networks, serves to illustrate the usefulness of the proposed methodology.

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