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Found 3 papers, 1 papers with code
Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems
Finally, we evaluate our Cold & Warm Net on public datasets in comparison to models commonly applied in the matching stage and it outperforms other models on all user types.
Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems
The mainstream RS ranking framework is composed of two parts: a Multi-Task Learning model (MTL) that predicts various user feedback, i. e., clicks, likes, sharings, and a Multi-Task Fusion model (MTF) that combines the multi-task outputs into one final ranking score with respect to user satisfaction.
Multi-Tasking Genetic Algorithm (MTGA) for Fuzzy System Optimization
Experiments on simultaneous optimization of type-1 and interval type-2 fuzzy logic controllers for couple-tank water level control demonstrated that the MTGA can find better fuzzy logic controllers than other approaches.
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