Semantics and Ontologies for Feedback-driven Adapting Recommender-Systems

01.01.2010 - 30.06.2012
Research funding project
Consumers increasingly buy products through the Internet, but they lack assistance for searching the "right" product. Recommender systems address this problem by asking targeted questions. The success (or failure) of such a recommendation process is defined in terms of conversion rate or click-out rate. It is very difficult to predict improvements for given changes of the recommender process, however, and manual changes are too expensive. Therefore, we propose automated adaptations of recommendation processes making use of semantic technology. Our approach makes Internet content accessible through an adaptive service for searching products. In particular, automated ontology learning from unstructured information sources such as newsgroups shall provide the basis, since ontologies are key to success. From their generic knowledge and related instances, high-level discourse models are automatically generated. These discourse models represent classes of potential dialogues between a customer and the recommender system and, in effect, a recommendation process. From these models, user interfaces for the end user are generated (semi-)automatically as well. After executing a discourse with its generated user interface for a period of time, a feedback component provides information about the usage of the system. This feedback leads to changes of the ontology, which in turn lead to changes of the discourse model. Consequently, the recommender process and its supporting user interface are changed as well. In addition, a human expert may influence this automatic adaption cycle. We can evaluate this approach through experiments with an existing recommender system owned by one of the project partners: Smart Assistant. Since this system is in successful real-world use, these experiments do not have to be restricted to a laboratory, but they can be performed in the real shopping environment.

People

Project leader

Project personnel

Institute

Contract/collaboration

  • Smart Information Systems GmbH

Grant funds

  • FFG - Österr. Forschungsförderungs- gesellschaft mbH (National) Group Thematic programme Austrian Research Promotion Agency (FFG) Specific program FIT-IT

Keywords

GermanEnglish
OntologieOntology
Semantische TechnologieSemantic Ontology
Recommender-SystemRecommender System
Diskurs-ModellDiscourse Model

Publications