Ajay Krishna Vajjala | Searcharxiv

arXiv2026

Fusing UI Structure & Semantics for Feature-Oriented App Screen Retrieval & Clustering

User Interface (UI) programming is challenging due to the complex abstraction gap between code and graphical software representations. To bridge this gap, UI programming tools often rely on screen retrieval and clustering, which require accurate similarity measures based on overlapping features. However, computing feature-oriented similarity is difficult because screens with similar functionality often exhibit design variations. To address this, we propose FRAME (ReinForced UseR InterfAce Screen EMbedding with Graphical Structural ComprEhension), a multi-modal, neuro-symbolic embedding technique. FRAME constructs symbolic, graph-based representations of UI components to encode salient relationships and capture feature patterns across different screens. It leverages large vision-language models for visual and lexical encoding, alongside a novel UI-specific computational geometry algorithm that enables weighted embedding propagation. Across three benchmarks, FRAME outperforms strong baselines by up to 13% MRR in search and 7.6 percentage points in clustering accuracy. A comprehensive ablation study further confirms the benefit of each component, demonstrating FRAME's potential for enhancing automated UI design and testing tools.

cs.SE↗

arXiv2024

Cross-Domain Recommendation Meets Large Language Models

Cross-domain recommendation (CDR) has emerged as a promising solution to the cold-start problem, faced by single-domain recommender systems. However, existing CDR models rely on complex neural architectures, large datasets, and significant computational resources, making them less effective in data-scarce scenarios or when simplicity is crucial. In this work, we leverage the reasoning capabilities of large language models (LLMs) and explore their performance in the CDR domain across multiple domain pairs. We introduce two novel prompt designs tailored for CDR and demonstrate that LLMs, when prompted effectively, outperform state-of-the-art CDR baselines across various metrics and domain combinations in the rating prediction and ranking tasks. This work bridges the gap between LLMs and recommendation systems, showcasing their potential as effective cross-domain recommenders.

cs.IR↗