Product Title Refinement via Multi-Modal Generative Adversarial Learning
This addresses the need for short, human-like product titles on mobile devices for E-commerce users, representing an incremental improvement by adding multi-modal data to existing methods.
The paper tackles the problem of generating concise product titles for mobile E-commerce by proposing a Multi-Modal Generative Adversarial Network (MM-GAN) that incorporates image, attribute tags, and textual information, resulting in a method that treats title generation as a reinforcement learning process evaluated by a discriminator.
Nowadays, an increasing number of customers are in favor of using E-commerce Apps to browse and purchase products. Since merchants are usually inclined to employ redundant and over-informative product titles to attract customers' attention, it is of great importance to concisely display short product titles on limited screen of cell phones. Previous researchers mainly consider textual information of long product titles and lack of human-like view during training and evaluation procedure. In this paper, we propose a Multi-Modal Generative Adversarial Network (MM-GAN) for short product title generation, which innovatively incorporates image information, attribute tags from the product and the textual information from original long titles. MM-GAN treats short titles generation as a reinforcement learning process, where the generated titles are evaluated by the discriminator in a human-like view.