Papers by Yangqin Jiang
RecLM: Recommendation Instruction Tuning (2025.acl-long)
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| Challenge: | Modern recommender systems aim to understand user-item relationships through past interactions, but their effectiveness is limited when handling sparse data or zero-shot scenarios. |
| Approach: | They propose a model-agnostic recommendation instruction-tuning paradigm that integrates large language models with collaborative filtering. |
| Outcome: | The proposed model-agnostic recommendation instruction-tuning paradigm improves performance across various settings and plug-and-play compatibility with state-of-the-art recommender systems. |
RecGPT: A Foundation Model for Sequential Recommendation (2025.emnlp-main)
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| Challenge: | Existing approaches fail in cold-start and cross-domain scenarios where new users or items lack sufficient interaction history. |
| Approach: | They propose a foundation model for sequential recommendation that achieves genuine zero-shot generalization capabilities by deriving item representations exclusively from textual features. |
| Outcome: | The proposed model achieves zero-shot generalization capabilities in cold-start and cross-domain scenarios. |
OpenPhone: Mobile Agentic Foundation Models (2026.findings-acl)
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| Challenge: | Mobile GUI agents face a critical dilemma: on-device models (4B or smaller) lack sufficient performance, while capable models are either too large for mobile deployment or prohibitively costly. |
| Approach: | They propose a mobile GUI agent system that leverages device-cloud collaboration to tap cost-efficiency of on-device models and high capability of cloud models. |
| Outcome: | The proposed system matches or nears larger models with reduced cloud costs on mobile platforms. |