Intelligent Online Selling Point Extraction for E-Commerce Recommendation

TL;DR

This paper presents the development and deployment of IOSPE, an automated system for extracting selling points for products on JD.com, significantly reducing costs and improving user engagement metrics.

Contribution

The paper introduces IOSPE, a scalable, real-time system for automatic selling point extraction, deployed at large scale on JD.com, demonstrating substantial operational and performance benefits.

Findings

Generated over 0.1 billion selling points since deployment.

Achieved a 1.89% increase in click-through rate.

Improved customer engagement by over 2.03% in average duration.

Abstract

In the past decade, automatic product description generation for e-commerce have witnessed significant advancement. As the services provided by e-commerce platforms become diverse, it is necessary to dynamically adapt the patterns of descriptions generated. The selling point of products is an important type of product description for which the length should be as short as possible while still conveying key information. In addition, this kind of product description should be eye-catching to the readers. Currently, product selling points are normally written by human experts. Thus, the creation and maintenance of these contents incur high costs. These costs can be significantly reduced if product selling points can be automatically generated by machines. In this paper, we report our experience developing and deploying the Intelligent Online Selling Point Extraction (IOSPE) system to serve the recommendation system in the JD.com e-commerce platform. Since July 2020, IOSPE has become a core service for 62 key categories of products (covering more than 4 million products). So far, it has generated more than 0.1 billion selling points, thereby significantly scaling up the selling point creation operation and saving human labour. These IOSPE generated selling points have increased the click-through rate (CTR) by 1.89\% and the average duration the customers spent on the products by more than 2.03\% compared to the previous practice, which are significant improvements for such a large-scale e-commerce platform.