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The quantification of this uncertainty can be useful in a variety of ways. Estimates of uncertainty might be used externally; for example, showing them to the user to increase user trust in the abilities of the system. They may also be used internally; for example, deciding the balance of \u2018safe\u2019 and less safe recommendations. In this work, we explore several methods for estimating uncertainty. The novelty comes from proposing methods that work in the implicit feedback setting. We use experiments on two datasets to compare a number of recommendation algorithms that are modified to perform uncertainty estimation. In our experiments, we show that some of these modified algorithms are less accurate than their unmodified counterparts, but others are actually more accurate. We also show which of these methods are best at enabling the recommender to be \u2018aware\u2019 of which of its recommendations are likely to be correct and which are likely to be wrong.<\/jats:p>","DOI":"10.1007\/978-3-031-26438-2_22","type":"book-chapter","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T06:32:56Z","timestamp":1677047576000},"page":"279-291","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Recommendation Uncertainty in\u00a0Implicit Feedback Recommender Systems"],"prefix":"10.1007","author":[{"given":"Victor","family":"Coscrato","sequence":"first","affiliation":[]},{"given":"Derek","family":"Bridge","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. 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