  On e-commerce platforms, predicting if two products are compatible with each
other is an important functionality to achieve trustworthy product
recommendation and search experience for consumers. However, accurately
predicting product compatibility is difficult due to the heterogeneous product
data and the lack of manually curated training data. We study the problem of
discovering effective labeling rules that can enable weakly-supervised product
compatibility prediction. We develop AMRule, a multi-view rule discovery
framework that can (1) adaptively and iteratively discover novel rulers that
can complement the current weakly-supervised model to improve compatibility
prediction; (2) discover interpretable rules from both structured attribute
tables and unstructured product descriptions. AMRule adaptively discovers
labeling rules from large-error instances via a boosting-style strategy, the
high-quality rules can remedy the current model's weak spots and refine the
model iteratively. For rule discovery from structured product attributes, we
generate composable high-order rules from decision trees; and for rule
discovery from unstructured product descriptions, we generate prompt-based
rules from a pre-trained language model. Experiments on 4 real-world datasets
show that AMRule outperforms the baselines by 5.98% on average and improves
rule quality and rule proposal efficiency.
