Abstract
Creating search and recommendation models responsibly requires monitoring more than just effectiveness and efficiency. Before moving these models into production, it is imperative to audit training data and evaluate their predictions for bias. Prior work has uncovered and studied the effects of different types of bias that can manifest in search and recommendation results. Despite of the debiasing approaches only recently emerged, there is still a long way to develop trustworthy search and recommendation models. This workshop aims to collect the recent advances in this field and offer a fresh ground for interested scientists from academia and industry. More information about the workshop is available at https://biasinrecsys.github.io/ecir2023/.
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Boratto, L., Faralli, S., Marras, M., Stilo, G. (2023). Fourth International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2023). In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13982. Springer, Cham. https://doi.org/10.1007/978-3-031-28241-6_39
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