  Sequential recommendation is a task to capture hidden user preferences from
historical user item interaction data. Significant progress has been made in
this domain by leveraging classification based learning methods. Inspired by
the recent paradigm of 'pretrain, prompt and predict' in NLP, we consider
sequential recommendation as a sequence to sequence generation task and propose
a novel model named Generative Recommendation (GenRec). Unlike classification
based models that learn explicit user and item representations, GenRec utilizes
the sequence modeling capability of Transformer and adopts the masked item
prediction objective to effectively learn the hidden bidirectional sequential
patterns. Different from existing generative sequential recommendation models,
GenRec does not rely on manually designed hard prompts. The input to GenRec is
textual user item sequence and the output is top ranked next items. Moreover,
GenRec is lightweight and requires only a few hours to train effectively in
low-resource settings, making it highly applicable to real-world scenarios and
helping to democratize large language models in the sequential recommendation
domain. Our extensive experiments have demonstrated that GenRec generalizes on
various public real-world datasets and achieves state-of-the-art results. Our
experiments also validate the effectiveness of the the proposed masked item
prediction objective that improves the model performance by a large margin.
