Demo Papers

An Adaptive Hybrid Movie Recommender based on Semantic Data

AutorAndreas Lommatzsch, Benjamin Kille, Jae-Won Kim, and Sahin Albayrak
QuelleOAIR 2013, Lisbon, Portugal 
LinksBibTeX   |   Uni-Bibliothek 

Recommender systems assist users in finding relevant entities, such as books or movies, according to their individual preferences. The entities' properties along with their relationships have to 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, such as the Internet Movie Database (IMDb) and Freebase. The system implements different algorithms to generate recommendations supporting the user in finding relevant, but potentially unknown movies. The system provides users with explicit explanations helping them to understand why a movie is relevant and to refine the request criteria according to their individual preferences. The system considers run-time complexity to guarantee a short request response time for individually adapted requests.