E-commerce search systems typically separate query-item relevance matching from downstream personalized ranking. While effective for large-scale serving, this design leaves relevance levels dominated by objective query-item matching, forcing follow-up ranking to operate over candidate structures that may not reflect user behavioral preference. We define a new personalized behavioral relevance task, which predicts whether a relevance-stage candidate item is supported by the user’s behavioral evidence under current query intent. We develop a corresponding framework, BRIDGE, for Behavioral Relevance Inference with Dynamic Guidance from Evidence. It organizes multi-timescale behavioral context from online logs, obtains reliable supervision with multi-perspective annotation and adjudication, and trains an LLM to produce structured evidence-grounded decision rationales. It particularly incorporates sample-specific rubric rewards into GRPO to improve generated reasoning traces. Offline experiments validate that BRIDGE improves both prediction accuracy and reasoning quality. Online A/B tests show gains of 0.19% in active user-days and 0.67% in transaction-related user-days, both significant in statistics.