Abstract. Social interactions are one of the key factors to the success of conferences and similar community gatherings. This paper describes a novel application that integrates data from the semantic web, online social networks, and a real-world contact sensing platform. This application was successfully deployed at ESWC09, and actively used by 139 people. Personal profiles of the participants were automatically generated using several Web 2.0 systems and semantic academic data sources, and integrated in real-time with face-to-face contact networks derived from wearable sensors. Integration of all these heterogeneous data layers made it possible to offer various services to conference attendees to enhance their social experience such as visualisation of contact data, and a site to explore and connect with other participants. This paper describes the architecture of the application, the services we provided, and the results we achieved in this deployment. 1

Abstract not found

Abstract. Personalised recommender systems learn about a user’s needs, and identify and suggest information items (news articles, images, videos, etc.) that meet those needs. User needs can be explicitly or implicitly defined either in the form of user tastes, interests and goals, or by system parameters and configurations. Most research efforts in the Recommender Systems field can be said to have been directed towards either defining and improving techniques that provide item recommendations from available preference data, or defining techniques for learning the latter. However, little research has focussed on learning which preferences are really relevant to provide accurate recommendations, and which ones imply anomalous behaviour of the recommendation mechanisms. We present a meta-evaluation methodology that applies Machine Learning techniques to analyse log information of a personalised news recommender system in order to discover (and rank) which user preferences and system settings are suitable for accurate recommendations. We also show how the proposed methodology can be used to ease the system evaluation itself.

Computer Science,

Abstract. Recommender systems suggest users information items they may be interested in. User profiles or usage data are compared with some reference characteristics, which may belong to the items (content-based approach), or to other users in the same context (collaborative filtering approach). These items are usually presented as a ranking, where the more relevant an item is predicted to be for a user, the higher it appears in the ranking. In this scenario, a preferential order has to be inferred, and therefore, preference learning methods can be naturally helpful. The relevant recommendation model features for the learningbased enhancements explored in this work comprise parameters of the recommendation algorithms, and user-related attributes. In the researched approach, machine learning techniques are used to discover which model features are relevant in providing accurate recommendations. The assessment of relevant model features, which is the focus of this paper, is envisioned as the first step in a learning cycle in which improved recommendation models are produced and executed after the discovery step, based on the findings that result from it.

Version: Accepted Manuscript Link(s) to article on publisher’s website:

Version: Accepted Manuscript Link(s) to article on publisher’s website: