Large Language Model Aided QoS Prediction for Service Recommendation

TL;DR

This paper introduces llmQoS, a novel approach leveraging large language models to improve web service quality prediction by extracting attribute information from natural language descriptions, effectively addressing data sparsity.

Contribution

The paper presents a new LLM-based model for QoS prediction that enhances recommendation accuracy by utilizing descriptive attributes of users and services.

Findings

llmQoS outperforms baseline models on WSDream dataset

The approach effectively mitigates data sparsity issues

Utilizes natural language descriptions for feature extraction

Abstract

Large language models (LLMs) have seen rapid improvement in the recent years, and have been 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. On the WSDream dataset, llmQoS is shown to overcome the data sparsity issue inherent to the QoS prediction problem, and outperforms comparable baseline models consistently.