Utilizing Linked Data Structures for Social-aware Search Applications
Proceedings of the 47. Jahrestagung der Gesellschaft für Informatik e.V. (GI)
Improving the user experience and conversion rate by means of personalization is of major importance for modern e-commerce applications. Several publications in the past have already dealt with the topic of adaptive search result ranking and appropriate ranking metrics. Newer approaches also took personalized ranking attributes of a connected Social Web platform into account to form so called Social Commerce Applications. However, these approaches were often limited to data silos of closed-platform data providers and none of the contributions discussed the benefits of Linked Data in building social-aware e-commerce applications so far. Therefore, we present a first formalization of a scoring model for a social-aware search approach that takes user interaction from multiple social networks into account. In contrast to other existing solutions, our approach focuses on a Linked Data information management in order to easily combine social data from different social networks. We analyze the possible influence of friend activities to the relevance of a person’s search intent and how it can be combined with other ranking factors in a formalized scoring model. As a result, we implement a first demonstrator built upon RDF data to show how an application can present the user an adaptive search result list depending on the users’ current social context.
Langer, André; Krug, Michael; Moreno, Luis; Gaedke, Martin: Utilizing Linked Data Structures for Social-aware Search Applications. Proceedings of the 47. Jahrestagung der Gesellschaft für Informatik e.V. (GI), pp. 1903-1914, 2017.