Neidhardt, J., & Sertkan, M. (2022). Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures. In L. Boratto, S. Faralli, M. Marras, & giovanni stilo (Eds.), Advances in Bias and Fairness in Information Retrieval (pp. 35–42). Springer Cham. https://doi.org/10.1007/978-3-031-09316-6_4
The quality of recommender systems has traditionally only been assessed using accuracy measures. Research has shown that accuracy is only one side of the medallion and that we should also consider quality features that go beyond accuracy. Recently, also fairness-related aspects and bias have increasingly been considered as outcome dimensions in this context. While beyond-accuracy measures including diversity, novelty and serendipity and bias in recommendation have been subject to the research discourse, their interrelation and temporal and group dynamics are clearly under-explored. In this position paper, we propose an approach that groups users based on their behaviors and preferences and that addresses beyond-accuracy needs of those groups while controlling for bias. Further, we consider the analysis of long-term dynamics of different interrelated beyond-accuracy measures and bias as crucial research direction since it helps to advance the field and to address societal issues related to recommender systems and personalization.
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Project title:
Christian Doppler Labor für Weiterentwicklung des State-of-the-Art von Recommender-Systemen in mehreren Domänen: CDL Neidhardt (CDG Christian Doppler Forschungsgesellschaft)
