Effective skills-aware talent recommendation must balance behavioral transition patterns, trajectory-sensitive adaptation, and inspectable occupation-level criteria. Evidence from public benchmarks on how these signals interact, however, remains limited. This study proposes CF-RL-TOPSIS, an interpretable late-fusion model that integrates a transition-aware collaborative branch, a compact reinforcement-style occupation-family bandit, and an entropy-weighted TOPSIS branch constructed from six semantic proxies; the validation-selected fusion coefficients remain auditable. The model is evaluated on two frozen public ICT talent-history benchmarks, JobHop and Karrierewege, using repeated chronological top-5 ranking and paired Wilcoxon tests. On JobHop the full hybrid attains NDCG@5 = 0.3040 +/- 0.0073 and significantly surpasses repeat-last, item Markov, transition-aware collaborative filtering, the CF+TOPSIS hybrid, GRU4Rec, and SASRec (p <= 0.0039 across planned comparisons). On Karrierewege the hybrid remains competitive but does not significantly exceed the strongest Markov baseline, revealing a persistence-dominated setting in which the bandit branch appropriately shrinks to near-zero weight. Proxy-sensitivity, family-level deep Q-network, and runtime checks support this interpretation, and a worked user-level case shows how branch scores, criterion weights, and rank shifts can be inspected for an individual recommendation. The contribution is not a benchmark-agnostic superiority claim, but a reproducible account of the conditions under which transparent late fusion adds value beyond simple continuation heuristics. In semantically rich, non-saturating talent-history regimes the three branches reinforce one another; in persistence-dominated regimes the same architecture remains competitive through its collaborative backbone, with the adaptive branch correctly inactive.

翻译：有效的技能感知型人才推荐需要平衡行为转换模式、轨迹敏感适配以及可审查的职业层级标准。然而，关于这些信号如何相互作用的公共基准证据仍然有限。本研究提出CF-RL-TOPSIS，一个可解释的后期融合模型，集成了转换感知协同分支、紧凑的强化学习风格职业族老虎机分支，以及由六个语义代理构建的熵权TOPSIS分支；验证选定的融合系数保持可审计性。该模型在两个冻结的公共ICT人才历史基准数据集JobHop和Karrierewege上进行了评估，采用重复的时间顺序top-5排名以及配对Wilcoxon检验。在JobHop上，完整混合模型达到了NDCG@5 = 0.3040 +/- 0.0073，并显著优于repeat-last、item Markov、转换感知协同过滤、CF+TOPSIS混合模型、GRU4Rec和SASRec（计划比较中p <= 0.0039）。在Karrierewege上，混合模型仍具有竞争力，但未显著超过最强的Markov基线，揭示了在一个持久性主导的场景中，老虎机分支适当收缩至接近零权重。代理敏感性、家庭级深度Q网络以及运行时检查支持了这一解释，并且一个具体的用户案例展示了如何针对个体推荐审查分支得分、标准权重和排名变化。本研究的贡献并非提出一个与基准无关的优越性声明，而是可重复地阐述了在何种条件下透明后期融合比简单延续启发式方法更具价值。在语义丰富、非饱和的人才历史场景中，三个分支相互增强；在持久性主导的场景中，同一架构通过其协同骨干保持竞争力，而自适应分支则正确保持非活跃状态。