Group RC-DMC extends RC-DMC by adding Set-Transformer group aggregation, low-rank regularization via nuclear-norm proximal steps, and a low-rank decoder to improve group-level RMSE on MovieLens and Goodbooks while staying competitive on precision, recall, and F1.
IEEE Computational Intelligence Magazine 19, 78–95
2 Pith papers cite this work. Polarity classification is still indexing.
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A late-fusion model of CF, RL bandit, and TOPSIS achieves NDCG@5=0.3040 on JobHop (outperforming baselines) but remains competitive without significant gains on Karrierewege, with the bandit branch deactivating in persistence-dominated data.
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Rank-Constrained Deep Matrix Completion for Group Recommendation
Group RC-DMC extends RC-DMC by adding Set-Transformer group aggregation, low-rank regularization via nuclear-norm proximal steps, and a low-rank decoder to improve group-level RMSE on MovieLens and Goodbooks while staying competitive on precision, recall, and F1.
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An Interpretable CF-RL-TOPSIS Fusion Model for Skills-Aware Talent Recommendation
A late-fusion model of CF, RL bandit, and TOPSIS achieves NDCG@5=0.3040 on JobHop (outperforming baselines) but remains competitive without significant gains on Karrierewege, with the bandit branch deactivating in persistence-dominated data.