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The credibility of recommender systems : Identifying biases and overspecialisation

Özgören, A. (2020) The credibility of recommender systems : Identifying biases and overspecialisation.

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Abstract:Recommender systems (RS) are artificial intelligence techniques that aim to reduce information overload and to provide users with diverse, serendipitous, and relevant recommendations in several application domains. However, there are still RS that only operate to increase the income of merchants without inspiring users to make relevant decisions. These RS provide users with biased and overspecialised recommendations which can lead to manipulation, irrelevant decisions, and low customer satisfaction. The motive of this study is to create a mechanism that allows users to identify biases and overspecialisation within RS so that they can avoid these potential problems and make relevant decisions. Based on the message credibility and triangulation theory, a bias & overspecialisation identification tool (BOIT) has been developed and used within an online experiment with 82 participants. The findings of this experiment indicate that participants were able to identify types of bias and overspecialisation within an e-commerce recommender system. As a result, the credibility of this recommender system decreased significantly. Therefore, it is concluded that the BOIT spreads awareness among users about potential biases and overspecialisation within RS and that it has a statistically significant effect on users’ judgment of the credibility of RS.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:85 business administration, organizational science
Programme:Business Administration MSc (60644)
Link to this item:http://purl.utwente.nl/essays/81314
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