Revamp: Enhancing Accessible Information Seeking Experience of Online Shopping for Blind or Low Vision Users
This addresses accessibility challenges in online shopping for blind or low vision users, representing an incremental improvement by leveraging existing review data.
The authors tackled the problem of online shopping accessibility for blind or low vision users by developing Revamp, a system that uses customer reviews to generate image descriptions and answer queries, which in evaluations with eight users provided useful descriptive information and helped locate key information efficiently.
Online shopping has become a valuable modern convenience, but blind or low vision (BLV) users still face significant challenges using it, because of: 1) inadequate image descriptions and 2) the inability to filter large amounts of information using screen readers. To address those challenges, we propose Revamp, a system that leverages customer reviews for interactive information retrieval. Revamp is a browser integration that supports review-based question-answering interactions on a reconstructed product page. From our interview, we identified four main aspects (color, logo, shape, and size) that are vital for BLV users to understand the visual appearance of a product. Based on the findings, we formulated syntactic rules to extract review snippets, which were used to generate image descriptions and responses to users' queries. Evaluations with eight BLV users showed that Revamp 1) provided useful descriptive information for understanding product appearance and 2) helped the participants locate key information efficiently.