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Felix Beierle

Publications and source records attributed to Felix Beierle.

20 records · Page 2Linked to original sources

Cross-Domain Discovery of Communication Peers. Identity Mapping and Discovery Services (IMaDS)

The upcoming WebRTC-based browser-to-browser communication services present new challenges for user discovery in peer-to-peer mode. Even more so, if we wish to enable different web communication services to interact. This paper presents Identity Mapping and Discovery Service (IMaDS), a global, scalable, service independent discovery service that enables users of web-based peer-to-peer applications to discover other users whom to communicate with. It also provides reachability and presence information. For that, user identities need to be mapped to any compatible service identity as well as to a globally unique, service-independent identity. This mapping and discovery process is suitable for multiple identifier formats and personal identifying properties, but it supports user-determined privacy options. IMaDS operates across different service domains dynamically, using context information. Users and devices have profiles containing context and other specific information that can be discovered by a search engine. The search results reveal the user's allocated globally unique identifier (GUID), which is then resolved to a list of the user's service domains identities, using a DHT-based directory service. Service-specific directories allow tracking of active endpoints, where users are currently logged on and can be contacted.

cs.NI↗

Exploring Choice Overload in Related-Article Recommendations in Digital Libraries

We investigate the problem of choice overload - the difficulty of making a decision when faced with many options - when displaying related-article recommendations in digital libraries. So far, research regarding to how many items should be displayed has mostly been done in the fields of media recommendations and search engines. We analyze the number of recommendations in current digital libraries. When browsing fullscreen with a laptop or desktop PC, all display a fixed number of recommendations. 72% display three, four, or five recommendations, none display more than ten. We provide results from an empirical evaluation conducted with GESIS' digital library Sowiport, with recommendations delivered by recommendations-as-a-service provider Mr. DLib. We use click-through rate as a measure of recommendation effectiveness based on 3.4 million delivered recommendations. Our results show lower click-through rates for higher numbers of recommendations and twice as many clicked recommendations when displaying ten instead of one related-articles. Our results indicate that users might quickly feel overloaded by choice.

cs.IR↗