  The training paradigm integrating large language models (LLM) is gradually
reshaping sequential recommender systems (SRS) and has shown promising results.
However, most existing LLM-enhanced methods rely on rich textual information on
the item side and instance-level supervised fine-tuning (SFT) to inject
collaborative information into LLM, which is inefficient and limited in many
applications. To alleviate these problems, this paper proposes a novel
practice-friendly two-stage LLM-enhanced paradigm (TSLRec) for SRS.
Specifically, in the information reconstruction stage, we design a new
user-level SFT task for collaborative information injection with the assistance
of a pre-trained SRS model, which is more efficient and compatible with limited
text information. We aim to let LLM try to infer the latent category of each
item and reconstruct the corresponding user's preference distribution for all
categories from the user's interaction sequence. In the information
augmentation stage, we feed each item into LLM to obtain a set of enhanced
embeddings that combine collaborative information and LLM inference
capabilities. These embeddings can then be used to help train various future
SRS models. Finally, we verify the effectiveness and efficiency of our TSLRec
on three SRS benchmark datasets.
