Dual Attention Network for Product Compatibility and Function Satisfiability Analysis

TL;DR

This paper introduces a Dual Attention Network (DAN) that automatically analyzes product compatibility and functionality from QA pairs, addressing the challenge of large product catalogs and brief textual data.

Contribution

The paper proposes a novel deep learning model, DAN, for automatic discovery of product compatibility and functions from QA data, improving accuracy and coverage.

Findings

DAN outperforms baseline models in compatibility prediction accuracy.

High coverage of identified compatible products and functions.

Effective handling of brief QA pairs and linguistic patterns.

Abstract

Product compatibility and their functionality are of utmost importance to customers when they purchase products, and to sellers and manufacturers when they sell products. Due to the huge number of products available online, it is infeasible to enumerate and test the compatibility and functionality of every product. In this paper, we address two closely related problems: product compatibility analysis and function satisfiability analysis, where the second problem is a generalization of the first problem (e.g., whether a product works with another product can be considered as a special function). We first identify a novel question and answering corpus that is up-to-date regarding product compatibility and functionality information. To allow automatic discovery product compatibility and functionality, we then propose a deep learning model called Dual Attention Network (DAN). Given a QA pair for a to-be-purchased product, DAN learns to 1) discover complementary products (or functions), and 2) accurately predict the actual compatibility (or satisfiability) of the discovered products (or functions). The challenges addressed by the model include the briefness of QAs, linguistic patterns indicating compatibility, and the appropriate fusion of questions and answers. We conduct experiments to quantitatively and qualitatively show that the identified products and functions have both high coverage and accuracy, compared with a wide spectrum of baselines.