A Unified Approach for Context-sensitive Recommendations

Abstract

We propose a model capable of providing context-sensitive content based on the similarity between an analysed context and the recommended content. It relies on the underlying thematic structure of the context by means of lexical and semantic analysis. For the context, we analyse both the static characteristics and dynamic evolution. The model has a high degree of generality by considering the whole range of possible recommendations (content) which best fits the current context. Based on the model, we have implemented a system dedicated to contextual advertisements for which the content is the ad while the context is represented by a web page visited by a given user. The dynamic component refers to the changes of the user's interest over time. From all the composite criteria the system could accept for assessing the quality of the result, we have considered relevance and diversity. The design of the model and its ensemble underlines our original view on the problem. From the conceptual point of view, the unified thematic model and its category based organization are original concepts together with the implementation.