Analyzing the Effectiveness of AI-Generated Tags for Traditional vs. Simplified Chinese Users on Weee

Abstract

Online grocery platforms are increasingly using AI-generated “popular” tags on their products like “Top Reordered
Dimsum” to boost product engagement. However, little is known about how these tags perform across different linguistic
and cultural user groups. This study investigates why such tags seem to drive higher sales from Simplified Chinese users
than Traditional Chinese users on Weee, an online Asian supermarket. We focused on two tags: a scenario-based one
(“MVP Seafood for Hotpot”) and a category-based one (“Top Reordered Dimsum”). We extracted reviews for the top five
products under each tag using Weee’s internal review API, then cleaned and separated the data by language. Using Jieba,
a Chinese text segmentation library, we identified the top 20 keywords in reviews for each group and compared their
distributions. We found no significant differences in keyword patterns between Traditional and Simplified Chinese reviews,
suggesting the tags are likely relevant to both groups. However, there were consistently far fewer Traditional Chinese
reviews, which may limit the visibility or effectiveness of the tags for that audience. These findings point to data sparsity,
in addition to content misalignment, as a possible reason for the differing sales impact of AI-generated tags across
language groups.