  Large language models (LLMs) have seen rapid improvement in the recent years,
and are used in a wider range of applications. After being trained on large
text corpus, LLMs obtain the capability of extracting rich features from
textual data. Such capability is potentially useful for the web service
recommendation task, where the web users and services have intrinsic attributes
that can be described using natural language sentences and are useful for
recommendation. In this paper, we explore the possibility and practicality of
using LLMs for web service recommendation. We propose the large language model
aided QoS prediction (llmQoS) model, which use LLMs to extract useful
information from attributes of web users and services via descriptive
sentences. This information is then used in combination with the QoS values of
historical interactions of users and services, to predict QoS values for any
given user-service pair. Our proposed model is shown to overcome the data
sparsity issue for QoS prediction. We show that on the WSDream dataset, llmQoS
outperforms comparable baseline models consistently.
