arXiv cs.AI· \"Ozkan Canay·· 14 小时前AI 评分18
CF-RL-TOPSIS：面向技能感知人才推荐的可解释融合模型
An Interpretable CF-RL-TOPSIS Fusion Model for Skills-Aware Talent Recommendation
AI 导读
研究提出可解释的后期融合模型 CF-RL-TOPSIS，整合转移感知协同过滤分支、轻量强化式职业族 bandit 分支与基于六项语义代理的熵权 TOPSIS 分支，融合系数经验证集选择且可审计。
正文 · 原文
Abstract: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.
| Comments: | v2: corrected rendering of mathematical symbols and of the author's initial; page layout improved; content unchanged. Preprint submitted to Knowledge-Based Systems; 23 pages, 4 figures, 8 tables |
| Subjects: | Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| MSC classes: | 68T05, 68T09, 90B50 |
| ACM classes: | H.3.3; I.2.6; I.2.8; H.4.2 |
| Cite as: | arXiv:2605.24155 [cs.IR] |
| (or arXiv:2605.24155v2 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24155 arXiv-issued DOI via DataCite |
Submission history
From: Özkan Canay [view email]
[v1]
Fri, 22 May 2026 19:22:58 UTC (154 KB)
[v2]
Mon, 26 Oct 2026 13:46:48 UTC (154 KB)
来源：arXiv cs.AI · arxiv.org