  Traditional session-based recommendation (SBR) utilizes session behavior
sequences from anonymous users for recommendation. Although this strategy is
highly efficient, it sacrifices the inherent semantic information of the items,
making it difficult for the model to understand the true intent of the session
and resulting in a lack of interpretability in the recommended results.
Recently, large language models (LLMs) have flourished across various domains,
offering a glimpse of hope in addressing the aforementioned challenges.
Inspired by the impact of LLMs, research exploring the integration of LLMs with
the Recommender system (RS) has surged like mushrooms after rain. However,
constrained by high time and space costs, as well as the brief and anonymous
nature of session data, the first LLM recommendation framework suitable for
industrial deployment has yet to emerge in the field of SBR. To address the
aforementioned challenges, we have proposed the LLM Integration Framework for
SBR (LLM4SBR). Serving as a lightweight and plug-and-play framework, LLM4SBR
adopts a two-step strategy. Firstly, we transform session data into a bimodal
form of text and behavior. In the first step, leveraging the inferential
capabilities of LLMs, we conduct inference on session text data from different
perspectives and design the component for auxiliary enhancement. In the second
step, the SBR model is trained on behavior data, aligning and averaging two
modal session representations from different perspectives. Finally, we fuse
session representations from different perspectives and modalities as the
ultimate session representation for recommendation. We conducted experiments on
two real-world datasets, and the results demonstrate that LLM4SBR significantly
improves the performance of traditional SBR models and is highly lightweight
and efficient, making it suitable for industrial deployment.
