An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Trust-based agent community for collaborative recommendation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
IEEE Intelligent Systems
Linked data on the web (LDOW2008)
Proceedings of the 17th international conference on World Wide Web
Programming collective intelligence
Programming collective intelligence
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
PITTCULT: trust-based cultural event recommender
Proceedings of the 2008 ACM conference on Recommender systems
Do Metrics Make Recommender Algorithms?
WAINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications Workshops
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
Unifying and targeting cultural activities via events modelling and profiling
EiMM '09 Proceedings of the 1st ACM international workshop on Events in multimedia
LODE: Linking Open Descriptions of Events
ASWC '09 Proceedings of the 4th Asian Conference on The Semantic Web
Tutorial on evaluating recommender systems
Proceedings of the fourth ACM conference on Recommender systems
Content-based recommendation in social tagging systems
Proceedings of the fourth ACM conference on Recommender systems
The YouTube video recommendation system
Proceedings of the fourth ACM conference on Recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Hybrid event recommendation using linked data and user diversity
Proceedings of the 7th ACM conference on Recommender systems
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Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation.