Time, Place and Environment: Can Conceptual Modelling improve Context-Aware Recommendation?

Recommendation refers to the automatic process of discovering and suggesting new but relevant items to users, according to the preferences inferred from their previous activity. But, not only the items or their content are related to the user preferences but also the context in which the user have consumed the items. In this regard, the so-called context-aware recommenders refer to the systems in-tegrating such contextual information in the recommendation pro-cess. However, sometimes it is not clear what context information is the best in order to improve the recommendation process or how it should be included. In this sense, we present a novel approach for context-aware recommendation based on a conceptual modelling for the user-item-context matrix. The modelling is conducted by means of the application of Formal Concept Analysis (FCA) to in-fer and organize the user preferences derived from their previous contextualized activity. We have experimented with different ways to integrate the contextual information by splitting it in the different dimensions that it addresses (e.g. location, time, company…). To that end, we have proposed different recommendation algorithms (content-based, collaborative filtering and hybrid recommenders) and also different ways to manage the user’s ratings. The obtained results prove the suitability of FCA for context-aware recommen-dation, outperforming other state-of-the-art proposals