Evaluating Recommender Systems with User Experiments

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Knowledge-aware Autoencoders for Explainable Recommender Systems (2018, background): “As stated in [9], 73 has to be considered as the minimum acceptable sample size for such kind of experiments.”
Organizational Decision-Making Structures in the Age of Artificial Intelligence (2019, background): “31 Similar conditions apply to real-time dynamic pricing in the airline and hotel industry, ad auctions, and high-frequency trading—where the speed of bidding and/or buying is critical.”
Current challenges and visions in music recommender systems research (2017, background): “, giving users a pleasant and useful personalized experience [107].”
ExpLOD: A Framework for Explaining Recommendations based on the Linked Open Data Cloud (2016, unknown): “Given that in [6] the minimum acceptable sample size for each experimental condition was set as 73, we can state that our experiment guaranteed the signiﬁcance of the results.”