Jaroslav Kuchar

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Modelling and understanding various contexts of users is important to enable personalised selection of Web APIs in directories such as Programmable Web. Currently, relationships between users and Web APIs are not clearly understood and utilized by existing selection approaches. In this paper, we present a semantic model of a Web API directory graph that(More)
Interest Beat (inbeat.eu) is a service for recommendation of content. InBeat was designed with emphasis on versatility, scalability and extensibility. The core contains the General Analytics INterceptor module, which collects and aggregates user interactions, the Preference Learning module and the Recommender System module. In this paper, we describe InBeat(More)
GAIN (inbeat.eu) is a web application and service for capturing and preprocessing user interactions with semantically described content. GAIN outputs a set of instances in tabular form suitable for further processing with generic machine-learning algorithms. GAIN is demoed as a component of a "SMART-TV" recommender system. Content is automatically described(More)
TV and Web convergence is becoming more and more a reality. This paper provides an overview of the opportunities and challenges that arise in future TV environments regarding unobtrusive, context-aware personalisation of digital media content. Subsequently, it describes the vision and first conceptual personalisation approach within the LinkedTV EU project.(More)
EasyMiner is a web-based visual interface for association rule learning. This paper presents a preview of the next release, which uses the R environment as the data processing backend. EasyMiner/R uses the arules package to learn rules. It uses the Classifications Based on Associations (CBA) algorithm as a classifier and to perform rule pruning.(More)