Human Rating System

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Semantic enrichment/homogenization by EKT

2017 (EN)
Human Rating System (EN)

Papikas, Ioannis (EN)

School of Science and Technology, MSc in Information & Communication Technology Systems (EL)
Berberidis, Christos (EN)
Tjortjis, Christos (EN)
Bassiliades, Nikolaos (EN)

Feedback systems exist in the market for a long time. Many retail stores are using similar systems in order to bring more sales, promote their business and drive customers into becoming more active and interested in their products. At the same time, feedback systems have been also used to provide a personal opinion or a recommendation about a person and their skills. This document is going to analyze the existing rating systems and more specifically LinkedIn endorsements feature, how it works and how it affects people and drives job applications and recruitments. In the analysis various flaws will be presented, that exist with the current rating systems and how the community behaves and reacts based on the existing flaws and the concept of a flawed system. Finally, the result of the analyses is a new model for rating and ranking users for their skills in order to fix the existing flaws. Although the community might say that the idea is broken beyond repair, this is a game changer introducing the concept of PageRank in combination with the ratings. The new model ranks users for their skills based on the graph itself allowing propagation of knowledge and experience as well as setting limits and bounds to the amount of experience sharing. (EN)

masterThesis

Feedback, Ratings (EN)

Διεθνές Πανεπιστήμιο της Ελλάδος (EL)
International Hellenic University (EN)

2017-03-25


IHU (EN)



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