We show how Formal Concept Analysis (FCA) can be applied to Collaborative Recommenders. FCA is a mathematical method for analysing binary relations. Here we apply it to the relation between users and items in a collaborative recommender system. FCA groups the users and items into concepts, ordered by a concept lattice. We present two new algorithms for finding neighbours in a collaborative recommender. Both use the concept lattice as an index to the recommender's ratings matrix. Our experimental results show a major decrease in the amount of work needed to find neighbours, while guaranteeing no loss of accuracy or coverage. © 2006 Springer-Verlag London.
CITATION STYLE
Du Boucher-Ryan, P., & Bridge, D. (2006). Collaborative recommending using formal concept analysis. In Research and Development in Intelligent Systems XXII - Proceedings of AI 2005, the 25th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 205–218). Springer London. https://doi.org/10.1007/978-1-84628-226-3_16
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