2,072 research outputs found
The live social semantics application: A platform for integrating face-to-face presence with on-line social networking
We describe a novel application that integrates real-world data on the face-to-face proximity of individuals with their identities and contacts in on-line social networks. This application was successfully deployed at two conference gatherings, ESWC09 and HT2009, and actively used by hundreds of people. Personal profiles of the participants were automatically generated using several Web 2.0 systems and semantic data sources, and integrated in real-time with face-to-face proximity relations detected using RFID-enabled badges. The integration of these heterogeneous data sources enables various services that enhance the experience of conference attendees, allowing them to explore their social neighbourhood and to connect with other participants. This paper describes the architecture of the application, the services we provided, and the results we achieved in these deployments
Live Social Semantics
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
Beautiful and damned. Combined effect of content quality and social ties on user engagement
User participation in online communities is driven by the intertwinement of
the social network structure with the crowd-generated content that flows along
its links. These aspects are rarely explored jointly and at scale. By looking
at how users generate and access pictures of varying beauty on Flickr, we
investigate how the production of quality impacts the dynamics of online social
systems. We develop a deep learning computer vision model to score images
according to their aesthetic value and we validate its output through
crowdsourcing. By applying it to over 15B Flickr photos, we study for the first
time how image beauty is distributed over a large-scale social system.
Beautiful images are evenly distributed in the network, although only a small
core of people get social recognition for them. To study the impact of exposure
to quality on user engagement, we set up matching experiments aimed at
detecting causality from observational data. Exposure to beauty is
double-edged: following people who produce high-quality content increases one's
probability of uploading better photos; however, an excessive imbalance between
the quality generated by a user and the user's neighbors leads to a decline in
engagement. Our analysis has practical implications for improving link
recommender systems.Comment: 13 pages, 12 figures, final version published in IEEE Transactions on
Knowledge and Data Engineering (Volume: PP, Issue: 99
Learning Points and Routes to Recommend Trajectories
The problem of recommending tours to travellers is an important and broadly
studied area. Suggested solutions include various approaches of
points-of-interest (POI) recommendation and route planning. We consider the
task of recommending a sequence of POIs, that simultaneously uses information
about POIs and routes. Our approach unifies the treatment of various sources of
information by representing them as features in machine learning algorithms,
enabling us to learn from past behaviour. Information about POIs are used to
learn a POI ranking model that accounts for the start and end points of tours.
Data about previous trajectories are used for learning transition patterns
between POIs that enable us to recommend probable routes. In addition, a
probabilistic model is proposed to combine the results of POI ranking and the
POI to POI transitions. We propose a new F score on pairs of POIs that
capture the order of visits. Empirical results show that our approach improves
on recent methods, and demonstrate that combining points and routes enables
better trajectory recommendations
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