B4I: Blackbox Platform Interpretation for Item-Level Assessment with Behavior Feedback

Many creator-facing and analytics-driven online platforms operate as black-box systems for external stakeholders: ranking and exposure mechanisms, internal models, and the ways in which these components interact with user behavior are inaccessible, while only final feedback such as clicks or conversions is observable. Under this constraint, stakeholders must infer item-level outcomes without access to platform internals or direct model outputs. Existing approaches often rely on opaque neural predictors or aggregate observed statistics, offering limited insight into how user-conditioned interaction patterns shape item-level outcomes. To address this problem, we introduce feature-based neural–logical components into an interpretable behavioral surrogate that models the observable mapping from user and item features to platform-mediated feedback. Building on this idea, we propose B4I, a two-stage framework. Stage I learns the surrogate from historical user–item interactions, producing user-conditioned interaction scores and neural–logical explanatory signals. Stage II evaluates the surrogate over a fixed historical user-group panel and aggregates the resulting outputs to predict item-level popularity labels. B4I thereby transfers behavioral knowledge learned from historical user–item interactions to item-level assessment without requiring observed user–item interaction feedback for the target item, enabling interpretable item-level assessment under black-box platform constraints. The code is anonymously available at https://anonymous.4open.science/r/B4I-2B2C/.