Abstract
The objective of our work is to identify the most relevant content given unstructured, text-based context. In this respect, we propose a unified model that includes a generic context model and the similarity metrics in order to provide context-sensitive content. The context model relies on the underlying thematic structure of the context by means of lexical and semantic analysis. Moreover, we analyse both the static characteristics and dynamic evolution. The model has a high degree of generality by not being committed to a certain domain, nor a constrained context structure. Based on the model, we have implemented a system dedicated to contextual advertisements for which the content is the set of relevant ads 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.
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Dinsoreanu, M., Potolea, R. (2013). Towards a Unified Thematic Model for Recommending Context-Sensitive Content. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2012. Communications in Computer and Information Science, vol 415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54105-6_5
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DOI: https://doi.org/10.1007/978-3-642-54105-6_5
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