EvoRec: A Unified Self-Evolving Agentic Recommender with Dynamic Reflection Trees

Key points

The authors propose EvoRec, a unified agentic recommendation framework that turns recommendation into a closed loop of need understanding, planning, execution, feedback, reflection, and repair.
A Need Agent infers structured user needs from long-term, short-term, and working memory, and a Planning Agent splits the retrieval budget of 3,000 between Behavior Continuation and Need Satisfaction while choosing tools and ranking fusion weights.
A Dynamic Reflection Tree localizes failures to need inference, planning, or prior repair strategies, generates personalized repairs, and verifies their effectiveness by re-running the decision.
Verified repair strategies enter working memory to guide later decisions and are abstracted into reusable strategies shared across users, so self-evolution continues without updating agent parameters.
On the Kuaishou local-services advertising dataset, EvoRec improves Hit@50 by 15.7% over the strongest baseline, and on the 18.5% of users with accumulated repair experience it improves Hit@50 by 13.1%.
In a seven-day online A/B test, nearline-deployed EvoRec increases advertising revenue by 4.6% and CTCVR by 3.5%, with a 14.5% revenue gain among high-intent users.

External research summaries. These are not HDATF publications or measured product results.