Advancements in text-guided diffusion models have allowed for the creation of visually appealing images similar to those created by professional artists. The effectiveness of these models depends on the composition of the textual description, known as the prompt, and its accompanying keywords. Evaluating aesthetics computationally is difficult, so human input is necessary to determine the ideal prompt formulation and keyword combination. In this study, we propose a human-in-the-loop method for discovering the most effective combination of prompt keywords using a genetic algorithm. Our approach demonstrates how this can lead to an improvement in the visual appeal of images generated from the same description.

Today, most recommender systems employ Machine Learning to recommend posts, products, and other items, usually produced by the users. Although the impressive progress in Deep Learning and Reinforcement Learning, we observe that recommendations made by such systems still do not correlate with actual human preferences. In our tutorial, we will bridge the gap between crowdsourcing and recommender systems communities by showing how one can incorporate human-in-the-loop into their recommender system to gather the real human feedback on the ranked recommendations. We will discuss the ranking data lifecycle and run through it step-by-step. A significant portion of tutorial time is devoted to a hands-on practice, when the attendees will, under our guidance, sample recommendations and build the ground truth dataset using crowdsourced data, and compute the offline evaluation scores.