title: User Modelling for Personalised Recommendation Systems
Creators: name: Yang, Fan
Publisher: name: University of Liverpool Repository
Year: 2024

description: The rapid advancement of communication, networking, and cloud computing technologies has precipitated an era of information explosion, resulting in diverse internet applications and services, including videos, news, music, and e-commerce. Addressing "information overload," personalised recommendation systems have become essential, significantly influencing user engagement and platform profitability. Consequently, these systems are a focal point for both academic and industrial research.
Effective recommendation systems hinge on user modelling, crucial for understanding users' needs and providing precise services. However, users' hidden intentions, multifaceted interests, and diverse attributes pose significant challenges. This thesis systematically explores user modelling for personalised recommendation systems, proposing innovative solutions across three key aspects: identifying their current intentions, predicting their subjective interests, and understanding users' attributes.
1. Hierarchical Users’ Intention and Preference Modelling: Existing models often overlook the link between users’ decisions and their intentions. Drawing from cognitive science, we introduce a method using relation-aware heterogeneous information network embedding and a relation-aware attention layer to capture user intentions and preferences. We create intention-preference hierarchical trees and employ structured decision patterns to predict user preferences under specific intentions. Our proposed model demonstrates significant improvements in Recall and MRR metrics compared to baseline models.
2. Knowledge-Enhanced User Multi-Interest Modelling: Traditional methods model user interests based on historical interactions, reflecting explicit interests but ignoring potential ones. They also face the cold start issue and limited recommendation diversity. We propose a knowledge-enhanced approach leveraging a knowledge graph as side information of items, extracting explicit interests from user-item interactions and inferring potential interests from related entities. An attention mechanism enhances multi-level user interest representations. Additionally, incorporating user attributes helps address the cold start problem. Our model significantly improves user interest modelling, demonstrating superior performance for both top-K recommendation and click-through rate prediction tasks.
3. Generative User Modelling with Large Language Models (LLMs): To address issues such as cold start, data sparsity, and incomplete modelling, we propose a multi-step reasoning approach by leveraging Chain-of-thought prompting to reason a comprehensive user model integrating user attributes, interests and intentions. Subsequently, we design and construct a collection of prompts to align the LLMs with various recommendation tasks, encompassing rating prediction, sequential recommendation, direct recommendation, and explanation generation. Extensive experiments conducted on real-world datasets demonstrate the immense potential of large language models in generating user models. Furthermore, the adoption of generative user modelling significantly enhances the performance of LLMs for four recommendation tasks across various evaluation metrics.