LikeThis! Empowering App Users to Submit UI Improvement Suggestions Instead of Complaints

• URL: http://arxiv.org/abs/2603.04245v1
• Date: Wed, 04 Mar 2026 16:33:35 GMT
• Title: LikeThis! Empowering App Users to Submit UI Improvement Suggestions Instead of Complaints
• Abstract summary: We propose LikeThis!, a GenAI-based approach that takes a user comment with the corresponding screenshot to immediately generate multiple improvement alternatives.<n>The results show that our approach helps generate better feedback from both user and developer perspectives.
• Score: 6.85181982998051
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: User feedback is crucial for the evolution of mobile apps. However, research suggests that users tend to submit uninformative, vague, or destructive feedback. Unlike recent AI4SE approaches that focus on generating code and other development artifacts, our work aims at empowering users to submit better and more constructive UI feedback with concrete suggestions on how to improve the app. We propose LikeThis!, a GenAI-based approach that takes a user comment with the corresponding screenshot to immediately generate multiple improvement alternatives, from which the user can easily choose their preferred option. To evaluate LikeThis!, we first conducted a model benchmarking study based on a public dataset of carefully critiqued UI designs. The results show that GPT-Image-1 significantly outperformed three other state-of-the-art image generation models in improving the designs to address UI issues while keeping the fidelity and without introducing new issues. An intermediate step in LikeThis! is to generate a solution specification before sketching the design as a key to achieving effective improvement. Second, we conducted a user study with 10 production apps, where 15 users used LikeThis! to submit their feedback on encountered issues. Later, the developers of the apps assessed the understandability and actionability of the feedback with and without generated improvements. The results show that our approach helps generate better feedback from both user and developer perspectives, paving the way for AI-assisted user-developer collaboration.

Related papers

• Efficient Personalization of Generative User Interfaces [17.40636742119123]
  We develop a sample-efficient personalization method that represents a new user in terms of prior designers.<n>In a technical evaluation, our preference model outperforms both a pretrained UI evaluator and a larger multimodal model.<n>Our findings suggest that lightweight preference elicitation can serve as a practical foundation for personalized generative UI systems.
  arXiv  Detail & Related papers  (2026-04-10T20:05:51Z)
• Improving User Interface Generation Models from Designer Feedback [9.027073356362182]
  Designer feedback is essential to improving performance on UI generation.<n>We find that existing methods based on ratings or rankings are not well-aligned with designers' rationales.
  arXiv  Detail & Related papers  (2025-09-20T19:02:53Z)
• RecGPT Technical Report [57.84251629878726]
  We propose RecGPT, a next-generation framework that places user intent at the center of the recommendation pipeline.<n> RecGPT integrates large language models into key stages of user interest mining, item retrieval, and explanation generation.<n>Online experiments demonstrate that RecGPT achieves consistent performance gains across stakeholders.
  arXiv  Detail & Related papers  (2025-07-30T17:55:06Z)
• Next-User Retrieval: Enhancing Cold-Start Recommendations via Generative Next-User Modeling [3.847929624516339]
  Lookalike algorithms provide a promising solution by extending feedback for new items based on lookalike users.<n>We propose Next-User Retrieval, a novel framework for enhancing cold-start recommendations via generative next-user modeling.<n>Our method achieves significant improvements with increases of 0.0142% in daily active users and +0.1144% in publications in Douyin.
  arXiv  Detail & Related papers  (2025-06-18T08:42:01Z)
• What Users Value and Critique: Large-Scale Analysis of User Feedback on AI-Powered Mobile Apps [2.352412885878654]
  We present the first comprehensive, large-scale study of user feedback on AI-powered mobile apps.<n>We leverage a curated dataset of 292 AI-driven apps across 14 categories with 894K AI-specific reviews from Google Play.<n>Our pipeline surfaces both satisfaction with one feature and frustration with another within the same review.
  arXiv  Detail & Related papers  (2025-06-12T14:56:52Z)
• Do MLLMs Capture How Interfaces Guide User Behavior? A Benchmark for Multimodal UI/UX Design Understanding [45.81445929920235]
  We introduce WiserUI-Bench, a novel benchmark for assessing models' multimodal understanding of UI/UX design.<n>It includes 300 diverse real-world UI image pairs, each consisting of two design variants A/B-tested at scale by actual companies.<n>Our benchmark supports two core tasks: (1) selecting the more effective UI/UX design by predicting the A/B test verified winner and (2) assessing how well a model, given the winner, can explain its effectiveness in alignment with expert reasoning.
  arXiv  Detail & Related papers  (2025-05-08T08:00:32Z)
• Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User [117.82681846559909]
  Conversational recommendation systems (CRSs) use multi-turn interaction to capture user preferences and provide personalized recommendations.<n>We propose a generative reward model based simulated user, named GRSU, for automatic interaction with CRSs.
  arXiv  Detail & Related papers  (2025-04-29T06:37:30Z)
• Interactive Visualization Recommendation with Hier-SUCB [52.11209329270573]
  We propose an interactive personalized visualization recommendation (PVisRec) system that learns on user feedback from previous interactions.<n>For more interactive and accurate recommendations, we propose Hier-SUCB, a contextual semi-bandit in the PVisRec setting.
  arXiv  Detail & Related papers  (2025-02-05T17:14:45Z)
• From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go [21.811104609265158]
  We developed Vocalizer, a mobile application that enables users to provide reviews through voice input.<n>Our findings show that users frequently utilized the AI agent to add more detailed information to their reviews.<n>We also show how interactive AI features can improve users self-efficacy and willingness to share reviews online.
  arXiv  Detail & Related papers  (2024-12-06T21:59:47Z)
• Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation [67.88747330066049]
  Fine-grained feedback captures nuanced distinctions in image quality and prompt-alignment. We show that demonstrating its superiority to coarse-grained feedback is not automatic. We identify key challenges in eliciting and utilizing fine-grained feedback.
  arXiv  Detail & Related papers  (2024-06-24T17:19:34Z)
• Identifying User Goals from UI Trajectories [19.492331502146886]
  We propose a new task goal identification from observed UI trajectories.<n>We also introduce a novel evaluation methodology designed to assess whether two intent descriptions can be considered paraphrases.<n>To benchmark this task, we compare the performance of humans and state-of-the-art models, specifically GPT-4 and Gemini-1.5 Pro.
  arXiv  Detail & Related papers  (2024-06-20T13:46:10Z)
• UltraFeedback: Boosting Language Models with Scaled AI Feedback [99.4633351133207]
  We present textscUltraFeedback, a large-scale, high-quality, and diversified AI feedback dataset. Our work validates the effectiveness of scaled AI feedback data in constructing strong open-source chat language models.
  arXiv  Detail & Related papers  (2023-10-02T17:40:01Z)
• ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models [26.11434743591804]
  We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart.
  arXiv  Detail & Related papers  (2021-02-15T13:43:49Z)
• Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing [66.57888248681303]
  We propose a novel emerging issue detection approach named MERIT. Based on the AOBST model, we infer the topics negatively reflected in user reviews for one app version. Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT.
  arXiv  Detail & Related papers  (2020-08-23T06:34:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.