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  4. Social preference ontologies for enriching user and item data in recommendation systems
 
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2014
Conference Paper
Titel

Social preference ontologies for enriching user and item data in recommendation systems

Abstract
Some of the known issues of recommendation algorithms are a result of the so called "Cold Start Problem" that is caused by a lack of sufficient data of users, items or the content, which are essential for the calculation of context-sensitive predictions. Along with this comes the "Sparsity Problem" which also exposes the problem of recommendation systems which are being provided with too little information of user feedback such as likes and views. As a consequent collaborative and knowledgebased filtering algorithms are unable of precise prediction which is causing a decline of the customer satisfaction. If beyond that there also is a lack of metadata, the calculation of similarities through content-based filtering algorithms is likely to fail as well. This paper introduces preference ontologies and how they help to reduce these issues by analyzing external data, in terms of texts from social networks and other web sources. Thereby we introduce a self-designed semantic engine, performing sentiment analysis and semantic keyword extraction. These novel ontologies represent the mined information and thus, describe the users interest in automatic analyzed topics and map them to the meta data of items in recommendation engines.
Author(s)
Krauss, Christopher
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS
Arbanowski, Stefan
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS
Hauptwerk
14th IEEE International Conference on Data Mining Workshop, ICDMW 2014. Proceedings. Vol.1
Konferenz
International Conference on Data Mining (ICDM) 2014
Workshop on Sentiment Elicitation from Natural Text for Information Retrieval and Extraction (SENTIRE) 2014
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DOI
10.1109/ICDMW.2014.76
Language
English
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Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS
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