Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling

TL;DR

This paper introduces MUSE, a novel graph neural network model that leverages multiple semantic relations among questions, answers, and reviews to improve answer ranking in e-commerce product Q&A platforms.

Contribution

The paper proposes a multi-relation graph model with a specialized GCN to effectively incorporate reviews and semantic relations for answer ranking.

Findings

MUSE outperforms existing models on real-world datasets.

Model effectively captures semantic relevance, content consistency, and textual entailment.

Achieves significant improvements in answer ranking accuracy.

Abstract

Many E-commerce sites now offer product-specific question answering platforms for users to communicate with each other by posting and answering questions during online shopping. However, the multiple answers provided by ordinary users usually vary diversely in their qualities and thus need to be appropriately ranked for each question to improve user satisfaction. It can be observed that product reviews usually provide useful information for a given question, and thus can assist the ranking process. In this paper, we investigate the answer ranking problem for product-related questions, with the relevant reviews treated as auxiliary information that can be exploited for facilitating the ranking. We propose an answer ranking model named MUSE which carefully models multiple semantic relations among the question, answers, and relevant reviews. Specifically, MUSE constructs a multi-semantic relation graph with the question, each answer, and each review snippet as nodes. Then a customized graph convolutional neural network is designed for explicitly modeling the semantic relevance between the question and answers, the content consistency among answers, and the textual entailment between answers and reviews. Extensive experiments on real-world E-commerce datasets across three product categories show that our proposed model achieves superior performance on the concerned answer ranking task.