Learning–Optimization Interactive Framework for Evolutionary Multi-Objective Recommendation

Evolutionary multi-objective optimization (EMO) has demonstrated strong capability for balancing conflicting criteria such as accuracy, diversity and novelty in recommender systems (RSs). However, most existing EMO-based RSs adopt a two-stagelearning–then–optimizationparadigm, in which representation learning and EMO are executed sequentially and independently. In other words, the later optimization stage is constrained by the established prediction scores from the former learning stage, leading to a performance bottleneck in multi-objective recommendation. To address this limitation, we propose a learning–optimization interactive framework (LOIF) that enables a closed-loop feedback interaction between representation learning and EMO. At the core of LOIF, we design an adaptive Pareto-feedback interaction (APFI) strategy to determinewhenandhowinformation is exchanged between learning and optimization. Learning-state signals, including loss stagnation and embedding stability, determinewhenprediction results are passed from learning to optimization. The resulting Pareto-optimal solutions determinehowinformation from EMO is fed back into learning via probabilistic sampling. Building upon APFI, we further propose a semantic-enhanced initialization (SEI) strategy to improve representation quality during the learning stage. We also propose a fast search competitive optimization (FSCO) strategy to accelerate Pareto front convergence during the optimization stage, enabling efficient multi-objective search under frequent learning–optimization interactions. Extensive experiments on real-world datasets demonstrate that LOIF consistently outper-forms traditional two-stage frameworks in accuracy–diversity trade-offs and achieves faster Pareto convergence.