  With the success of large language models, generative retrieval has emerged
as a new retrieval technique for recommendation. It can be divided into two
stages: the first stage involves constructing discrete Codes (i.e., codes), and
the second stage involves decoding the code sequentially via the transformer
architecture. Current methods often construct item semantic codes by
reconstructing based quantization on item textual representation, but they fail
to capture item discrepancy that is essential in modeling item relationships in
recommendation sytems. In this paper, we propose to construct the code
representation of items by simultaneously considering both item relationships
and semantic information. Specifically, we employ a pre-trained language model
to extract item's textual description and translate it into item's embedding.
Then we propose to enhance the encoder-decoder based RQVAE model with
contrastive objectives to learn item code. To be specific, we employ the
embeddings generated by the decoder from the samples themselves as positive
instances and those from other samples as negative instances. Thus we
effectively enhance the item discrepancy across all items, better preserving
the item neighbourhood. Finally, we train and test semantic code with with
generative retrieval on a sequential recommendation model. Our experiments
demonstrate that our method improves NDCG@5 with 43.76% on the MIND dataset and
Recall@10 with 80.95% on the Office dataset compared to the previous baselines.
