Translative Neural Team Recommendation: From Multilabel Classification to Sequence Prediction
Kap Thang, Hawre Hosseini, Hossein Fani
SIGIR '25 - Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval
We reformulated team recommendation as a sequence-to-sequence task using transformer architectures, achieving up to 82x improvement over existing feedforward neural approaches. Our method was evaluated on 4 large-scale datasets (DBLP, USPTO, IMDB, GitHub) with distinct skill/expert distributions, consistently outperforming baselines across all metrics.