Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
IEEE Transactions on Knowledge and Data Engineering
A new approach for combining content-based and collaborative filters
Journal of Intelligent Information Systems
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
The Effectiveness of Personalized Movie Explanations: An Experiment Using Commercial Meta-data
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
News@hand: A Semantic Web Approach to Recommending News
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Recommending scientific articles using citeulike
Proceedings of the 2008 ACM conference on Recommender systems
SXRS: An XLink-based Recommender System using Semantic Web technologies
Expert Systems with Applications: An International Journal
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Consumer Electronics
TasteWeights: a visual interactive hybrid recommender system
Proceedings of the sixth ACM conference on Recommender systems
An adaptive hybrid movie recommender based on semantic data
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
| Hi-index | 0.00 |
With the constantly growing amount of online available products and data, recommender algorithms are one of the key technologies helping users to cope with information overload. In contrast to information retrieval systems, recommender systems discover new items matching users' preferences. An item's relevance depends on a collection of criteria, such as its properties, user preferences and 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 a "global" recommender for the aggregated dataset (consisting of several heterogeneous datasets) results in a lower recommendations quality than creating an ensemble of recommenders. We study the complete recommendation creation process from the semantic dataset to a web application discussing the challenges and pitfalls of each step.