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- 193 creator laszlo-toeroek.
- 193 creator martin-hepp.
- 193 type InProceedings.
- 193 label "Towards Portable Shopping Histories: Using GoodRelations to Expose Ownership Information to E-Commerce Sites".
- 193 sameAs 193.
- 193 abstract "Recommender systems are an important technology component for many e-commerce applications. In short, they are technical means that suggest potentially relevant products and services to the users of a Web site, typically a shop. The recommendations are computed in advance or during the actual visit and use various types of data as input, in particular past purchases and the purchasing behavior of other users with similar preferences. One major problem with recommender systems is that the quality of recommendations depends on the amount, quality, and representativeness of the in-formation about items already owned by the visitor, e.g. from past purchases at that particular shop. For first-time visitors and customers migrating from other merchants, the amount of available information is often too small to generate good recommendations. In other words, shopping history data for a single user is fragmented and spread over multiple sites, and cannot be actively exposed by the user himself to additional shops. This creates strong lock-in effects for consumers, because they cannot migrate from one merchant to another without losing access to high-quality recommendations, and it creates strong market entry barriers for new merchants, since they do not have access to the customers’ shopping history with other shops. In this paper, we propose to use Semantic Web technology, namely GoodRelations and schema.org, to empower e-commerce customers to (1) collect and manage ownership information about products, (2) advertise interest in sharing this information with shop sites in exchange for better recommendations or oth-er incentives, and (3) expose the information to such shop sites directly from their browser. We then sketch how a shop site could use the ownership infor-mation to propose relevant products.".
- 193 hasAuthorList authorList.
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- 193 keyword "E-Commerce".
- 193 keyword "GoodRelations".
- 193 keyword "RDF".
- 193 keyword "RDFa".
- 193 keyword "Recommender Systems".
- 193 keyword "Semantic Web".
- 193 keyword "schema.org".
- 193 title "Towards Portable Shopping Histories: Using GoodRelations to Expose Ownership Information to E-Commerce Sites".
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- 205 creator freddy-lecue.
- 205 creator marco-luca-sbodio.
- 205 creator pierpaolo-tommasi.
- 205 creator robert-tucker.
- 205 creator simone-tallevi-diotallevi.
- 205 creator veli-bicer.
- 205 type InProceedings.
- 205 label "Predicting Severity of Road Traffic Congestion using Semantic Web Technologies".
- 205 sameAs 205.
- 205 abstract "Predictive reasoning, or the problem of estimating future observations given some historical information, is an important inference task for obtaining insight on cities and supporting efficient urban planning. This paper, focusing on transportation, presents how severity of road traffic congestion can be predicted using semanticWeb technologies. In particular we present a system which integrates numerous sensors (exposing heterogenous, exogenous and raw data streams such as weather information, road works, city events or incidents) to improve accuracy and consistency of traffic congestion prediction. Our prototype of semantics-aware prediction, being used and experimented currently by traffic controllers in Dublin City Ireland, works efficiently with real, live and heterogeneous stream data. The experiments have shown accurate and consistent prediction of road traffic conditions, main benefits of the semantic encoding.".
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- 205 keyword "Ontology stream".
- 205 keyword "Predictive reasoning".
- 205 keyword "Semantic cities".
- 205 keyword "Semantic system in use".
- 205 keyword "Traffic congestion".
- 205 title "Predicting Severity of Road Traffic Congestion using Semantic Web Technologies".
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- 224 creator andreas-harth.