论文标题
弱监督兼容产品预测的自适应多视图规则发现
Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction
论文作者
论文摘要
在电子商务平台上，预测是否彼此兼容两种产品是获得值得信赖的产品推荐和搜索体验的重要功能。但是，由于异质产品数据以及缺乏手动策划的培训数据，难以准确预测产品兼容性。我们研究发现有效的标签规则的问题，这些规则可以实现弱监督的产品兼容性预测。我们开发了Amrule，这是一个多视图规则发现框架，可以（1）自适应地发现新颖的统治者，可以补充当前的弱监督模型以改善兼容性预测； （2）从结构化属性表和非结构化产品描述中发现可解释的规则。 Amrule通过提升风格的策略从大错误实例中自适应地发现标签规则，高质量的规则可以纠正当前模型的弱点，并迭代地改进模型。对于从结构化产品属性发现的规则，我们从决策树中生成可组合的高阶规则；为了从非结构化产品描述中发现规则，我们从预先训练的语言模型中生成了基于及时的规则。 4个现实世界数据集的实验表明，Amrule的表现平均优于基本线5.98％，并提高了规则质量和规则提案效率。
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.
