![[2602.11836] ULTRA:Urdu Language Transformer-based Recommendation Architecture](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.11836] ULTRA:Urdu Language Transformer-based Recommendation Architecture
Summary
The paper presents ULTRA, a transformer-based recommendation architecture tailored for the Urdu language, addressing challenges in semantic content retrieval for low-resource languages.
Why It Matters
As a low-resource language, Urdu lacks effective recommendation systems. ULTRA aims to enhance personalized content retrieval by utilizing advanced transformer techniques, improving relevance and adaptability in semantic search, which is crucial for better user experience in content consumption.
Key Takeaways
- ULTRA introduces a dual-embedding architecture for improved semantic content retrieval.
- The system effectively distinguishes between short and long user queries for better context understanding.
- Experimental results show over 90% precision in recommendations compared to traditional methods.
- The architecture provides insights for developing semantic retrieval systems for other low-resource languages.
- ULTRA's approach could be adapted to enhance recommendation systems across various domains.
Computer Science > Information Retrieval arXiv:2602.11836 (cs) [Submitted on 12 Feb 2026 (v1), last revised 25 Feb 2026 (this version, v2)] Title:ULTRA:Urdu Language Transformer-based Recommendation Architecture Authors:Alishbah Bashir, Fatima Qaiser, Ijaz Hussain View a PDF of the paper titled ULTRA:Urdu Language Transformer-based Recommendation Architecture, by Alishbah Bashir and 2 other authors View PDF HTML (experimental) Abstract:Urdu, as a low-resource language, lacks effective semantic content recommendation systems, particularly in the domain of personalized news retrieval. Existing approaches largely rely on lexical matching or language-agnostic techniques, which struggle to capture semantic intent and perform poorly under varying query lengths and information needs. This limitation results in reduced relevance and adaptability in Urdu content recommendation. We propose ULTRA (Urdu Language Transformer-based Recommendation Architecture),an adaptive semantic recommendation framework designed to address these challenges. ULTRA introduces a dual-embedding architecture with a query-length aware routing mechanism that dynamically distinguishes between short, intent-focused queries and longer, context-rich queries. Based on a threshold-driven decision process, user queries are routed to specialized semantic pipelines optimized for either title/headline-level or full-content/document level representations, ensuring appropriate semantic granularity during retrieval. The ...