LIFT: A Lifecycle-aware Interaction Factorization Transformer for Unified Retrieval and Ranking

Cascaded recommender systems use the same user history for retrieval and ranking, while the two stages operate with different information available at different points of an interaction. We propose the Lifecycle-aware Interaction Factorization Transformer (LIFT), which decomposes each interaction into ordered Request, Item, Context, and Action states and models them as a causal sequence. Retrieval reads the Request state, while ranking reads the Context state, allowing both tasks to share history modeling while preserving stage-specific information. LIFT instantiates this representation with Role-Conditioned Attention and a lightweight Pre-LN Bias. On ML-20M and Taobao, LIFT achieves the highest Joint Score among the evaluated joint models, improving over the strongest baselines by 4.9% and 3.6%, respectively. Loss-weight sweeps show favorable retrieval–ranking trade-offs, while ablations and scaling analyses further examine lifecycle sequence construction, model components, and capacity settings.