Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A flexible rule-based method for interlinking, integrating, and enriching user data
ICWE'10 Proceedings of the 10th international conference on Web engineering
Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Computing political preference among twitter followers
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Analyzing user modeling on twitter for personalized news recommendations
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Geo-Location estimation of flickr images: social web based enrichment
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Personalized Photo Recommendation By Leveraging User Modeling On Social Network
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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Social Web applications such as Twitter and Flickr are widely used services that generate large volumes of usage data. The challenge of modeling the use and the users of such Social Web services based on their data has received a lot of attention in recent years. In this paper, we go a step further and investigate how the Linked Open Data (LOD) cloud can be leveraged as additional knowledge source in user modeling processes that exploit user data from the Social Web. Specifically, we introduce a user modeling framework that utilizes semantic background knowledge from LOD and evaluate it in the area of point of interest (POI) recommendations. For this purpose, we infer user preferences in POIs based on the users' behavior observed on Twitter and Flickr, combined with referable evidence from the Web of Data. We compare strategies that aggregate knowledge from two LOD sources: GeoNames and DBpedia. The evaluation validates the advantages of our approach; we show that the user modeling quality improves when LOD-based background information is included in the process.