Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for learning and analyzing hybrid recommenders based on heterogeneous semantic data
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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Recommender systems assist users in finding relevant entities according to their individual preferences. The entities' properties along with their relationships must be considered in order to articulate good recommendations. In this paper, we present an approach for developing an adaptive hybrid recommender system with semantic data. Such data is represented as large graph of nodes (semantic entities) and edges (semantic relations) filled with contents collected from Linked-Open-Data sources. The system implements different algorithms to generate recommendations supporting users in finding relevant, but potentially unknown movies. The system provides users with explicit explanations helping them to understand why a movie is relevant. Users may refine requests according to their individual preferences. The system considers run-time complexity to guarantee a short request response time for individually adapted requests.