Evaluating rankers using implicit feedback, such as clicks on documents in a result list, is an increasingly popular alternative to traditional evaluation methods based on explici...
We formulate a model for multi-class object detection in a multi-camera environment. From our knowledge, this is the first time that this problem is addressed taken into account ...
Xavier Boix, Gemma Roig, Horesh Ben Shitrit, Pasca...
We propose a novel sequential decision approach to modeling ordinal ratings in collaborative filtering problems. The rating process is assumed to start from the lowest level, eva...
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
This paper proposes a novel, unified, and systematic approach to combine collaborative and content-based filtering for ranking and user preference prediction. The framework inco...