2018/02/16 by Kevin Jasberg, Jasberg, Kevin, Sergej Sizov +1
Computer Science · #Data Stream Mining Techniques #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Information Retrieval (cs.IR) #Mobile Crowdsensing and Crowdsourcing #Recommender Systems and Techniques
paper · pdf · doi:10.48550/arxiv.1802.05895
openalex publication_date 2018/02/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Latest research revealed a considerable lack of reliability within user feedback and discussed striking impacts for the assessment of adaptive web systems and content personalisation approaches, e.g. ranking errors, systematic biases to accuracy metrics as well as its natural offset (the magic barrier). In order to perform holistic assessments and to improve web systems, a variety of strategies have been proposed to deal with this so-called human uncertainty. In this contribution we discuss the most relevant strategies to handle uncertain feedback and demonstrate that these approaches are more or less ineffective to fulfil their objectives. In doing so, we consider human uncertainty within a purely probabilistic framework and utilise hypothesis testing as well as a generalisation of the magic barrier to compare the effects of recently proposed algorithms. On this basis we recommend a novel strategy of acceptance which turns away from mere filtering and discuss potential benefits for the community of the WWW.