Next-basket Recommendation (NBR) refers to the task of predicting a set of items that a user will purchase in the next basket. However, most of existing works merely focus on the relevance between user preferences and predicted items, ignoring the essential relationships among items in the next basket, which often results in over-homogenization of items. In this work, we presents a novel Generative next-basket Recommendation model (GeRec), a new NBR paradigm that generates the recommended items one by one to form the next basket via an autoregressive decoder. This generative NBR paradigm contributes to capturing and considering item relationships inside each baskets in both training and serving. Moreover, we jointly consider user’s both item- and basket-level contextual information to better capture user’s multi-granularity preferences. Extensive experiments on three real-world datasets demonstrate the effectiveness of our model.