Summary of Exploring User Retrieval Integration Towards Large Language Models For Cross-domain Sequential Recommendation, by Tingjia Shen et al.
Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation
by Tingjia Shen, Hao Wang, Jiaqing Zhang, Sirui Zhao, Liangyue Li, Zulong Chen, Defu Lian, Enhong Chen
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Cross-Domain Sequential Recommendation (CDSR) model, URLLM, aims to alleviate the cold-start issue by mining and transferring users’ sequential preferences across different domains. By introducing Large Language Models (LLMs) into CDSR, the authors address two crucial issues: seamless information integration and domain-specific generation. The novel framework uses a dual-graph sequential model, alignment and contrastive learning, and user retrieve-generation models to capture diverse information and transfer domain knowledge. A refinement module is also proposed to prevent out-of-domain generation. Experiments on Amazon demonstrate the effectiveness of URLLM in comparison to state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new approach called Cross-Domain Sequential Recommendation (CDSR) that helps make recommendations when we don’t have enough information about someone’s preferences. They want to use Large Language Models, which are good at understanding language, to help with this problem. To do this, they need to find a way to combine the strengths of these models with other techniques. They develop a new framework called URLLM that includes several components to make this work. They test their approach on Amazon and show that it performs better than other methods. |
Keywords
» Artificial intelligence » Alignment
