A semantic framework for personalized ad recommendation based on advanced textual analysis

Authors:
Dorothea Tsatsou;Fotis Menemenis;Ioannis Kompatsiaris;Paul C. Davis
Affiliations:
Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece;Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece;Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece;Motorola Applied Research & Technology Center, Schaumburg, IL, USA
Venue:
Proceedings of the third ACM conference on Recommender systems
Year:
2009

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Abstract

In this paper we present a hybrid recommendation system that combines ontological knowledge with content-extracted linguistic information, derived from pre-trained lexical graphs, in order to produce high quality, personalized recommendations. In the described approach, such recommendations are exemplified in an advertising scenario. We propose a distributed system architecture that uses semantic knowledge, based on terminologically enriched domain ontologies, to learn ontological user profiles and consequently infer recommendations through fuzzy semantic reasoning. A real world user study demonstrates the improvements attained in providing user-relevant recommendations with the aid of semantic profiles.