Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation
arXiv:2606.03866v1 Announce Type: cross Abstract: Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry. However, aligning the LLM's semantic space with the recommender's ID space via post-training (e.g., SFT and RL) remains challenging. Existing LLM4Rec paradigms are bottlenecked by two main issues: (1) the difficulty of measuring and improving chain-of-thought (CoT) quality in open-domain recommendation during SFT, and (2) the neglect of the trade-off between LLM semantic rewards and recommendation preference rewards during RL alignment. In
The proliferation of large language models (LLMs) in industrial applications highlights the current challenges in effectively integrating their semantic capabilities with existing system architectures.
Improving LLM-enhanced recommendation systems is crucial for enhancing user experience and driving engagement across numerous digital platforms, directly impacting revenue and market share.
This research suggests a new approach to policy optimization for LLM-enhanced recommendation systems, potentially improving alignment between semantic understanding and user preference.
- · E-commerce platforms
- · Content streaming services
- · AI/ML researchers
- · Recommendation system developers
- · Legacy recommendation systems
- · Companies unable to integrate LLMs effectively
More accurate and personalized recommendations increase user satisfaction and engagement on platforms.
Enhanced recommendation quality could lead to increased consumption of curated content and products, boosting platform revenue.
The optimized integration of LLMs may set new industry standards for recommendation systems, accelerating the adoption of similar advanced AI solutions across various sectors.
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Read at arXiv cs.CL