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By Year By Research Group | This paper examines the problem of social collaborative filtering (CF) to recommend items of interest to users in a social network setting. Unlike standard CF algorithms using relatively simple user and item features, recommendation in social networks... The performances of supervised learning techniques on image classification problems heavily rely on the quality of their training images. But the acquisition of high quality training images requires significant efforts from human annotators. In this paper... Preference learning has recently gained significant attention in the machine learning community. This is mainly due to its increasing applications in the real-world problems. In this paper, we investigate a Gaussian process framework for learning the ... In Bayesian approaches to utility learning from preferences, the objective is to infer a posterior belief distribution over an agent’s utility function based on previously observed agent preferences. From this, one can then estimate quantities such as the... In this paper we show that the optimization of several ranking-based performance measures, such as precision-at-k and average-precision, is intimately related to the solution of quadratic assignment problems. Both the task of test-time prediction of ... Topic models have the potential to improve search and browsing by extracting useful semantic themes from web pages and other text documents. When learned topics are coherent and interpretable, they can be valuable for faceted browsing, results set ... Wireless Sensor Networks (WSNs) provide a low cost option for gathering spatially dense data from different environments. However, WSNs have limited energy resources that hinder the dissemination of the raw data over the network to a central location.... In this paper we present an algorithm to learn a multi-label classifier which attempts at directly optimising the $F$-score. The key novelty of our formulation is that we explicitly allow for assortative (submodular) pairwise label interactions, i.e., we ... Although the concepts of cognitive load (CL), and user experience (UX) are widely recognized as critical in the automotive domain, little evaluation has taken place regarding the investigation of the effect of CL on UX in this domain. This position paper ... |