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Highly skewed long-tail item distribution is very common in recommendationsystems. It significantly hurts model performance on tail items. To improvetail-item recommendation, we conduct research to transfer knowledge from headitems to tail items, leveraging the rich user feedback in head items and thesemantic connections between head and tail items. Specifically, we propose anovel dual transfer learning framework that jointly learns the knowledgetransfer from both model-level and item-level: 1. The model-level knowledgetransfer builds a generic meta-mapping of model parameters from few-shot tomany-shot model. It captures the implicit data augmentation on the model-levelto improve the representation learning of tail items. 2. The item-leveltransfer connects head and tail items through item-level features, to ensure asmooth transfer of meta-mapping from head items to tail items. The two types oftransfers are incorporated to ensure the learned knowledge from head items canbe well applied for tail item representation learning in the long-taildistribution settings. Through extensive experiments on two benchmark datasets,results show that our proposed dual transfer learning framework significantlyoutperforms other state-of-the-art methods for tail item recommendation in hitratio and NDCG. It is also very encouraging that our framework further improveshead items and overall performance on top of the gains on tail items.

A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation