Østfold University College

About: Østfold University College is a education organization based out in Halden, Norway. It is known for research contribution in the topics: Context (language use) & Health care. The organization has 438 authors who have published 1213 publications receiving 12510 citations. The organization is also known as: HiØ.

A social-semantic recommender system for advertisements

Abstract: Social applications foster the involvement of end users in Web content creation, as a result of which a new source of vast amounts of data about users and their likes and dislikes has become available. Having access to users’ contributions to social sites and gaining insights into the consumers’ needs is of the utmost importance for marketing decision making in general, and to advertisement recommendation in particular. By analyzing this information, advertisement recommendation systems can attain a better understanding of the users’ interests and preferences, thus allowing these solutions to provide more precise ad suggestions. However, in addition to the already complex challenges that hamper the performance of recommender systems (i.e., data sparsity, cold-start, diversity, accuracy and scalability), new issues that should be considered have also emerged from the need to deal with heterogeneous data gathered from disparate sources. The technologies surrounding Linked Data and the Semantic Web have proved effective for knowledge management and data integration. In this work, an ontology-based advertisement recommendation system that leverages the data produced by users in social networking sites is proposed, and this approach is substantiated by a shared ontology model with which to represent both users’ profiles and the content of advertisements. Both users and advertisement are represented by means of vectors generated using natural language processing techniques, which collect ontological entities from textual content. The ad recommender framework has been extensively validated in a simulated environment, obtaining an aggregated f-measure of 79.2% and a Mean Average Precision at 3 (MAP@3) of 85.6%.

The filter bubble and its effect on online personal health information.

Abstract: Search engines and social networks are increasingly used for health related inquiry by the public. The information found through search engines, or presented by social network services are typically tailored to the individual through the use of complex algorithms taking into consideration comprehensive information about the individual performing the search, often without the knowledge of the searcher. In this paper, I discuss how the technology poses challenges both for patients and clinicians, and present some ideas to mitigate these problems.