LLM Agents Drive User-Centric Recommendations
机器之心 · wechat · 2026-07-12
This position paper argues that in the Agentic era, personalized recommendation may shift from a "platform-centric" to a "user-driven" approach.
The authors suggest that platforms only capture fragmented behaviors, whereas users can integrate cross-platform and offline life contexts. LLM Agents make leveraging this cross-platform data operationally viable for the first time. A proof-of-concept experiment validates this:
- In Amazon future purchase prediction, adding Google/YouTube data improved Hit@5, NDCG@5, and Recall@5.
- In YouTube recommendations, incorporating cross-platform data boosted overall precision, reinforcement recommendations, and exploration recommendations, with the most significant gains in exploration.
The paper also addresses counterarguments: big tech ecosystem integration doesn't equal full context, and user willingness to export data to cloud-based Agents remains a practical hurdle. The authors conclude that this isn't about replacing platform recommendations, but adding a user-controlled final decision layer on top of existing platform infrastructure.
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