LDCO: Evidence-Grounded Preference Transfer for User-Side Cold-Start Recommendation

Recommender systems connect users with products at scale, yet they remain largely platform-centered and dependent on target-domain histories. When a user enters an unfamiliar domain with an ambiguous query, three difficulties arise together: target-domain cold start leaves little evidence for personalization, the query omits relevant preferences, and the platform's ranking may not match the user's preferred ordering. We address these coupled problems with Layered Dimension-Conditioned Query Optimization (LDCO), a user-side large language model agent framework that operates through an existing retrieval interface. LDCO constructs domain-level and global profiles from authorized source-domain histories, selects relevant evidence, and maps transferable preferences to target-product attributes. It then uses Monte Carlo tree search on a fuzzy-query dataset to optimize the query-generation prompt through retrieval feedback, and reranks the returned candidates by query–profile relevance. On a user-disjoint Amazon benchmark, LDCO raises Hybrid Hit@100 from 0.3169 to 0.4155, MRR@100 from 0.0365 to 0.0758, and NDCG@100 from 0.0872 to 0.1388 over retrieval with the original fuzzy queries. Under live Amazon retrieval, the full framework raises Precision@15 from 0.2628 to 0.4162. These results show that evidence-grounded preference transfer, retrieval-aware query optimization, and user-side reranking jointly improve cold-start recommendation without modifying the platform's retrieval model.