The results highlight the effectiveness of IaGEL, particularly in sparse datasets like Yelp2018. However, the methodology raises concerns about the influence of hyperparameter selection on reported performance metrics. For instance, the optimal values for parameters such as the number of sampled intents (α) and neighbor search order (γ) appear dataset-specific but lack justification regarding their generalizability. Could the authors provide a sensitivity analysis or discussion on how these parameters impact performance across varying dataset densities? Additionally, Figure 5a visualizes the trade-off between diversity and stability, but the underlying user feedback metrics influencing these outcomes remain unclear. Could this aspect be elaborated to better understand the balance achieved by the recommendation strategy?
