PRISMIC: Reconstructing User Preference via Intent Decomposition and Consolidation

Large language models (LLMs) have recently improved sequential recommendation, yet the task remains a retrieval problem without explicit user queries: the system must infer the next item from user histories where diverse intents and preferences are intertwined. Existing methods typically compress this heterogeneous evidence into a single embedding or query, while LLM-based recommenders can produce overly general queries that are semantically plausible but weakly aligned with retrieval. We propose PRISMIC (Preference Reconstruction via Intent Synthesis and Multi-signal Inference Consolidation), which reformulates sequential recommendation as multi-signal intent consolidation. PRISMIC first generates multiple candidate queries from different historical signals, then trains an LLM-based consolidator with GRPO using an NDCG-based retrieval reward to merge relevant signals into a single query. The resulting consolidated query is not directly used at inference time; instead, it serves as a semantic supervision target for training a user encoder, enabling LLM-free inference. Across four real-world datasets, PRISMIC consistently outperforms strong baselines. Ablations further show that the gains come not from GRPO alone, but from consolidating multiple intent-signals and distilling the consolidated intent into an encoder.