  The recent advancements in Large Language Models (LLMs) have sparked interest
in harnessing their potential within recommender systems. Since LLMs are
designed for natural language tasks, existing recommendation approaches have
predominantly transformed recommendation tasks into open-domain natural
language generation tasks. However, this approach necessitates items to possess
rich semantic information, often generates out-of-range results, and suffers
from notably low efficiency and limited extensibility. Furthermore, practical
ID-based recommendation strategies, reliant on a huge number of unique
identities (IDs) to represent users and items, have gained prominence in
real-world recommender systems due to their effectiveness and efficiency.
Nevertheless, the incapacity of LLMs to model IDs presents a formidable
challenge when seeking to leverage LLMs for personalized recommendations. In
this paper, we introduce an Elegant Effective Efficient Extensible solution for
large language models for Sequential Recommendation (E4SRec), which seamlessly
integrates LLMs with traditional recommender systems that exclusively utilize
IDs to represent items. Specifically, E4SRec takes ID sequences as inputs,
ensuring that the generated outputs fall within the candidate lists.
Furthermore, E4SRec possesses the capability to generate the entire ranking
list in a single forward process, and demands only a minimal set of pluggable
parameters, which are trained for each dataset while keeping the entire LLM
frozen. We substantiate the effectiveness, efficiency, and extensibility of our
proposed E4SRec through comprehensive experiments conducted on four widely-used
real-world datasets. The implementation code is accessible at
https://github.com/HestiaSky/E4SRec/.
