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Auflistung nach Schlagwort "Scoring Model"

1 - 2 von 2
  • Konferenzbeitrag
    Optimal IT Project Selection – Quantification of Critical Scoring Criteria
    (Projektmanagement und Vorgehensmodelle 2022 - Virtuelle Zusammenarbeit und verlorene Kulturen?, 2022) Karrenbauer, Christin; Breitner, Michael Hans
    The management of IT project portfolios is challenging because of IT projects’ complexity, dynamics, unknowns, and uncertainties. IT projects account for a large IT budget proportion and significantly influence value contribution, strategic development, goal achievements, and competitive advantages. Many IT projects still fail, exceed time and resources, and do not reach their planned goals because of wrong decisions, unsatisfactory evaluation, and missing selection criteria. Thus, a continuous IT project scoring and selection is crucial to enable an optimal portfolio composition. We conduct a systematic literature review and 14 semi-structured qualitative expert interviews to develop a uniform and holistic scoring approach. Our findings show that IT projects’ urgency, strategy, efficiency, risk, and complexity are critical IT project scoring criteria. Our scoring approach increases objectivity and quality in evaluating planned and running IT projects and allows more convincing and transparent decisions.
  • Textdokument
    Utilizing Linked Data Structures for Social-aware Search Applications
    (INFORMATIK 2017, 2017) Langer, André; Krug, Michael; Moreno, Luis; Gaedke, Martin
    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.