Proceedings of the Twelfth International Conference on Concept Lattices and Their Applications Clermont-Ferrand, France, October 13-16, 2015
Formal Concept Analysis is a method of analysis of logical data based on formalization of conceptual knowledge by means of lattice theory. It has proved to be of interest to various applied fields such as data visualization, knowledge discovery and data mining, database theory, and many others. The International Conference “Concept Lattices and Their Applications (CLA)” is being organized since 2002 and its aim is to bring together researchers from various backgrounds to present and discuss their research related to FCA. The Twelfth edition of CLA was held in Clermont-Ferrand, France from October 13 to 16, 2015. The event was jointly organized by the LIMOS laboratory, CNRS, and Blaise Pascal university, France. This volume includes the selected papers and the abstracts of 5 invited talks. We would like to express our warmest thanks to the keynote speakers. This year, there were initially 39 submissions, from which the Program Committee selected 20 papers which represents an acceptance rate of 51.2%. The program of the conference consisted of five keynote talks given by the following distinguished researchers: Didier Dubois, Lhouari Nourine, Gabriella Pasi, Jan Ramon, and Takeaki Uno, together with twenty communications authored by researchers from 11 countries, namely: Austria, Czech republic, France, Germany, Kazakhstan, Republic of South Africa, Russia, Slovakia, Spain, Sweden, and Ukraine. Each paper was reviewed by 3–4 members of the Program Committee and/or additional reviewers. We thank them all for their valuable assistance. It is planned that extended versions of the best papers will be published in a well-established journal, after another reviewing process.
In this work we propose and study an approach for collaborative filtering, which is based on Boolean matrix factorisation and exploits additional (context) information about users and items. To avoid similarity loss in case of Boolean representation we use an adjusted type of projection of a target user to the obtained factor space. We have compared the proposed method with SVD-based approach on the MovieLens dataset. The experiments demonstrate that the proposed method has better MAE and Precision and comparable Recall and F-measure. We also report an increase of quality in the context information presence.
Concept lattices arising from noisy or high dimensional data have huge amount of formal concepts, which complicates the analysis of concepts and dependencies in data. In this paper, we consider several methods for pruning concept lattices and discuss results of their comparative study.