Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fab: content-based, collaborative recommendation
Communications of the ACM
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Survey of semantic annotation platforms
Proceedings of the 2005 ACM symposium on Applied computing
Inferring binary trust relationships in Web-based social networks
ACM Transactions on Internet Technology (TOIT)
The Geospatial Web: How Geobrowsers, Social Software and the Web 2.0 are Shaping the Network Society (Advanced Information and Knowledge Processing)
Collective knowledge systems: Where the Social Web meets the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
Discovering shared conceptualizations in folksonomies
Web Semantics: Science, Services and Agents on the World Wide Web
A collaborative constraint-based meta-level recommender
Proceedings of the 2008 ACM conference on Recommender systems
FOAFing the music: Bridging the semantic gap in music recommendation
Web Semantics: Science, Services and Agents on the World Wide Web
Recommendations based on semantically enriched museum collections
Web Semantics: Science, Services and Agents on the World Wide Web
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Improving recommender systems with adaptive conversational strategies
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Collaborative Feature-Combination Recommender Exploiting Explicit and Implicit User Feedback
CEC '09 Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing
The million dollar programming prize
IEEE Spectrum
Recommender Systems: An Introduction
Recommender Systems: An Introduction
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The increasing plethora of information available on the Web necessitates effective personalization mechanisms that allow users to retrieve pieces of information that are more likely to be of interest to them. Fortunately, the democratization of the Web (aka. Web 2.0) that provides online users with widely available tools that allow them contribute and integrate themselves into the global information space not only increases the sheer amounts of data, but also offers opportunities to extract semantic meaning. This paper therefore presents an integrated approach that harnesses geo-tagged web resources like tourism services or track data from bike trails to derive semantic annotations for objects from their geographic proximity. Following this, a recommendation mechanism is proposed that hybridizes collaborative mechanisms with the additional knowledge about semantic annotations to make predictions about what will be relevant to a user in a specific situation. The utility of this integrated approach is showcased by an adaptive Web-GIS scenario that supports travelers in their decision making. Finally, the proposed algorithms are evaluated using historical log data from real users who were exploring the map of a tourism destination. The results indicate that, despite very short interaction sequences, improvements compared to a collaborative filtering baseline can be achieved. An additional advantage lies in offering users more detailed options to express their search preferences that is not quantified by the presented evaluation scenario.