Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction

TL;DR

This paper introduces AMRule, a multi-view rule discovery framework that enhances weakly-supervised product compatibility prediction by adaptively generating interpretable rules from structured and unstructured data, improving accuracy and efficiency.

Contribution

The paper presents a novel adaptive multi-view rule discovery method that iteratively improves product compatibility prediction by generating high-quality rules from diverse data sources.

Findings

AMRule outperforms baselines by 5.98% on average.

It improves rule quality and proposal efficiency.

The framework effectively combines structured and unstructured data for better predictions.

Abstract

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.