Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
The Geometry of Information Retrieval
The Geometry of Information Retrieval
IEEE Transactions on Knowledge and Data Engineering
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
Journal of Biomedical Informatics
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Recommender Systems Handbook
Multi-Relational learning for recommendation of matches between semantic structures
KES'12 Proceedings of the 16th international conference on Knowledge Engineering, Machine Learning and Lattice Computing with Applications
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We propose a collaborative filtering data processing method based on reflective vector-space retraining, referred to as Progressive Reflective Indexing (PRI). We evaluate the method's ability to provide recommendations of items from a long tail. In order to reflect ‘real-world' demands, in particular those regarding non-triviality of recommendations, our evaluation is novelty-oriented. We compare PRI with a few widely-referenced collaborative filtering methods based on SVD and with Reflective Random Indexing (RRI) - a reflective data processing method established in the area of Information Retrieval. To demonstrate the superiority of PRI over other methods in long tail recommendation scenarios, we use the probabilistically interpretable AUROC measure. To show the relation between the structural properties of the user-item matrix and the optimal number of reflections we model the analyzed data sets as bipartite graphs.