Linked Data-based Conceptual Modelling for Recommendation: A FCA-Based Approach

Abstract

Abstract. In a recommendation task it is crucial to have an accurate contentbased description of the users and the items consumed by them. Linked Open Data (LOD) has been demonstrated as one of the best ways of obtaining this kind of content, given its huge amount of structured information. The main question is to know how useful the LOD information is in inferring user preferences and how to obtain it. In this context, we propose a novel approach for Content Modelling and Recommendation based on Formal Concept Analysis (FCA). The approach is based in the modelling of the user and content related information, enriched with Linked Open Data, and in a new algorithm, to analyze the models and recommend new content. The framework provided by the ESWC 2014 Recommendation Challenge is used for the evaluation of the proposal. The results are within the average range of other participants, so the suitability of FCA for this scenario seems to be addressed. Nevertheless, further work has to be carried out in order to propose a refined approach for the management of LOD information.

Formal Concept Analysis and Information Retrieval – A Survey

“…not just Boolean relations for rating the same item) providing ranked recommended items. Finally, in 2014 Castellanos et al [15] presented an approach based on [23] to extract preferences from a user activity log and derive semantically-enhanced item recommendations from them.…”