Enhancing Accessible Information Seeking Experience of Online Shopping for Blind or Low Vision Users


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.


Ruolin Wang, Zixuan Chen, Mingrui Ray Zhang, Zhaoheng Li, Zhixiu Liu, Zihan Dang, Chun Yu, and Xiang 'Anthony' Chen. 2021. Revamp: Enhancing Accessible Information Seeking Experience of Online Shopping for Blind or Low Vision Users. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems(CHI '21). Association for Computing Machinery, New York, NY, USA, Article 494, 1–14. DOI:


  • How to extract the informative reviews for better understanding product appearances?

To answer this question, we first need a deeper understanding on what visual information are of special interests to BVI users, then we could further explore whether reviews could serve as informative sources to these queries or not. Based on the formative study, we found that BVI users' visual questions on online products could be divided into two categories: (i) visual attributes of image, e.g., color, logo, shape, size; and (ii) high-level concepts that can be inferred from an image, e.g., usage method, style. In this work, we focus on the four main visual attributes mentioned above, and the key is to extract the descriptive and comparative expressions from the reviews. We iteratively established three groups of syntactic rules as follows:

Rule 1: Adjective + Keyword or Keyword + Verb + Adjective. The descriptive adjectives usually provide supplementary visual information. e.g., “a shimmery purple", “crescent shape”. The evaluative adjectives expressing subjective emotions can be vague hence not helpful to further understand the visual attributes. e.g., “great color”.

Rule 2: 1st pronoun + ... + Keyword + ... + that/which/but/because. Rather than the simple sentences only containing expressions on attitude e.g., “I feel disappointed at the color.”, the sentences with clauses usually provide more detailed and useful information.

Rule 3: Comparative Expressions.  (1) Keyword (shape) + “like/liked”, e.g., “shaped like a Cola Bottle”. Comparing the shape of a product with a familiar daily object can be helpful for better understanding the shape; (2) Keyword (size) + “fit/for/of”, e.g., “size fits in all cup holders”, shows reviews containing details on how the product fits in the settings are informative; (3) “than/more of” + Keyword (color), e.g., “it is a terra cotta than mocha”. Sighted customers complaining about this kind of difference between picture and the product can also be informative for better understanding the actual appearance.


  • How could these syntactic rules be generalized to the products on Amazon? How about those products without many reviews?
  • What are the take-aways for accessible information seeking? What lessons learnt from this project could be helpful for broader applications beyond the online shopping scenario?



Revamp: Related Projects

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Latest News

Sept 16, 2022: Three works submitted to CHI.

Aug 2, 2022: Became a PhD Candidate, finally.

July 29, 2022: Hope I could spend more time on Neuromatch computational neuroscience course.

April 7, 2022: Two works submitted to UIST.

March 28, 2022: Started a course on Neural Signal Processing.

Sept 21 2021: Finished my first industry internship at Microsoft EPIC Research Group. So grateful.

April 23 2021: Attended 2021 CRA-WP Grad Cohort for Women. 

March 19 2021: Finished a course on Neural Networks and Deep Learning.

Feb 13 2021: Attended weSTEM conference. Such an inspiring experience!

Dec 18 2020: Finished a course on Computational Imaging.

Dec 12 2020: Three works accepted by CHI.

Nov 22 2020: My first time attending NAISys.

Sept 17 2020: Three works submitted to CHI.

June 20 2020: One work rejected by UIST.

May 6 2020: One work submitted to UIST.

March 20 2020: Finished a course on Bioelectronics.

Feb 7 2020: One work accepted by CHI LBW.

Dec 13 2019: Finished a course on Neuroengineering.

Dec 8 2019: One work rejected by CHI.

Oct 25 2019: One work accepted by IMWUT.

Oct 22 2019: One work presented at UIST SIC.

Sep 20 2019: One paper submitted to CHI.

Aug 15 2019: One paper submitted to IMWUT.

July 30 2019: My first time attending SIGGRAPH.




UCLA Disabilities and Computing Program

NLP Group @ Computer Science, UCLA

Laboratory for Clinical and Affective Psychophysiology @ Psychology, UCLA

ACE, Makeability, Make4all @ UW

Human-Computer Interaction Initiative @ HKUST

 Interaction Lab @ KAIST



SIGCHI Accessibility Committee (2021 - )

UCLA ECE Faculty Recruitment Student Committee (2021)

Accessibility Co-chair (UIST 2020, 2021)

UCLA ECEGAPS Prelim Reform Committee (2020)

Publicity Co-Chair (ISS 2020)

Associate Chair (CHI LBWs 2020, 2022)


Student Volunteer (UIST 2019, 2020, NAISys 2020)

Volunteer at Beijing Volunteer Service Foundation and the China Braille Library (2018)



ECE 209AS Human-Computer Interaction, UCLA (2019 Fall, 2020 Fall, 2022 Winter)


Honors & Awards

Selected for a SIGCHI Student Travel Grant, 2020

Selected to CRA-WP Grad Cohort for Women, 2020

Graduates with distinction & Outstanding Thesis Award , Tsinghua University 2019

Best Paper Honorable Mention Award (Top 5%), CHI 2019

National Scholarship (Top 1%), Ministry of Education of the People’s Republic of China, 2018

Second Prize, Tsinghua University 35th Challenge Cup, 2018

Comprehensive Scholarship (Top 4%), Tsinghua University, 2017

First Prize, GIX Innovation Competition, 2016

Outstanding Thesis Award, Tianjin University, 2015


Invited Talks

"Inclusive Design: Accessibility Ignites Innovation" at TEDxTHU, 2018


Selected Press

TechCrunch: Alibaba made a smart screen to help blind people shop and it costs next to nothing

The Next Web: Alibaba’s inexpensive smart display tech makes shopping easier for the visually impaired 

Techengage: Alibaba's Smart Touch is everything for the visually impaired

Google’s AI hardware, in new hands