A Framework for Learning and Analyzing Hybrid Recommenders based on Heterogeneous Semantic Data

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

With the constantly growing amount of online available products and data, recommender algorithms are one of the key technologies helping the user to cope with the information overload. In contrast to information retrieval systems, recommender systems discover new items matching the user preferences. The relevance of an item depends on a collection of criteria, such as the item\'s properties, the user preferences and the respective context. In this paper we focus on recommending previously unrated items. We analyze how to compute highly relevant recommendations based on aggregated content-based knowledge. We compare different approaches for aggregating semantic knowledge and show how to find good scaling and recommender models taking into account the specific dataset properties. Furthermore, we discuss the gain of combining different semantic data sets. We evaluate our approaches on semantic movie datasets as well as on user feedback collected with our movie recommender web application. The evaluation shows that for each semantic relationship set a specific recommender model should be learned. Learning one global recommender for the aggregated dataset (consisting of several heterogeneous datasets) results in a lower recommendations quality than creating an ensemble of recommenders for each relationship set. We study the complete recommender creation process from the semantic dataset to a web application discussing the challenges and pitfalls of each step.