Fine Tuning Out-of-Vocabulary Item Recommendation with User Sequence Imagination

Recommending out-of-vocabulary (OOV) items is a challenging problem since the in-vocabulary (IV) items have well-trained behavioral embeddings but the OOV items only have content features. Current OOV recommendation models often generate'makeshift' embeddings for OOV items from content features and then jointly recommend with the `makeshift' OOV item embeddings and the behavioral IV item embeddings. However, merely using the'makeshift' embedding will result in suboptimal recommendation performance due to the substantial gap between the content feature and the behavioral embeddings.