Translative Neural Team Recommendation: From Multilabel Classification to Sequence Prediction

Neural team recommendation has achieved state-of-the-art performance in forming teams of experts whose success in completing complex tasks is almost surely guaranteed.The proposed models frame the problem as a Boolean multilabel classification, mapping the dense vector representations of required skills to the sparse occurrence (multi-hot) vector representation of an optimum subset of experts using multilayer feedforward neural networks.Such approaches, however, suffer from the curse of sparsity in the highdimensional vector of optimum experts in the output layer.In this paper, we propose to reformulate the team recommendation problem into a sequence prediction task and leverage seq-to-seq models, including transformers, to map an input sequence of the required subset of skills onto an output sequence of the optimum subset of experts.Our experiments on four large-scale datasets from various domains, with distinct distributions of skills in teams, show that the seq-to-seq approach is consistently superior overall in a host of classification and information retrieval metrics.Our codebase is available at https://github.com/fani-lab/OpeNTF/tree/nmt.