User Modeling in the Era of Large Language Models: Current Research and Future Directions (2312.11518v2)

Abstract: User modeling (UM) aims to discover patterns or learn representations from user data about the characteristics of a specific user, such as profile, preference, and personality. The user models enable personalization and suspiciousness detection in many online applications such as recommendation, education, and healthcare. Two common types of user data are text and graph, as the data usually contain a large amount of user-generated content (UGC) and online interactions. The research of text and graph mining is developing rapidly, contributing many notable solutions in the past two decades. Recently, LLMs have shown superior performance on generating, understanding, and even reasoning over text data. The approaches of user modeling have been equipped with LLMs and soon become outstanding. This article summarizes existing research about how and why LLMs are great tools of modeling and understanding UGC. Then it reviews a few categories of LLMs for user modeling (LLM-UM) approaches that integrate the LLMs with text and graph-based methods in different ways. Then it introduces specific LLM-UM techniques for a variety of UM applications. Finally, it presents remaining challenges and future directions in the LLM-UM research. We maintain the reading list at: https://github.com/TamSiuhin/LLM-UM-Reading

• The paper refines its structural classification to enhance clarity in graph data augmentation techniques.
• It expands coverage to heterogeneous and dynamic graphs, incorporating virtual node methods and edge addition strategies.
• The authors integrate recent benchmarking on graph out-of-distribution challenges, setting a foundation for future research.

Overview of Revisions in Graph Data Augmentation Methods

In the revised version of this survey paper on graph data augmentation (GDA), the authors have addressed the critical feedback from reviewers to improve the clarity and scope of their work. The survey originally aimed to consolidate various methods of GDA for enhancing tasks implemented on graph neural networks (GNNs) and other graph-based systems. The revisions have added meaningful context and broadened the scope, which now encapsulates emerging trends in graph processing.

Primarily, the paper's revisions lie in two key areas: structural clarification and expansion of content on complex graph types. The authors improved structural consistency by explicitly correlating method categories with their nomenclature, particularly in Sections 4.2 and 4.3. This modification enhances the readability and comprehension of the methods' explanations.

A pertinent point of contention noted by reviewers involved the categorization in Section 3. The statement concerning graph data augmentation terminology was deemed ambiguous, prompting the authors to revise it for precision, which aligns with standard categorization terminologies such as counterfactual augmentation and pseudo-labeling.

The paper substantially extends its survey to include heterogeneous and dynamic graphs in Section 7. The addition of Subsection 7.5 bridges the existing literature with critical discussions on complex graph representations, reflecting the field's growing interest in these challenging areas. By elucidating the challenges and paths forward in this segment, the authors provide a robust analysis of complex graph types that are pertinent yet intricate to model.

Moreover, the survey introduces a discussion on the application dynamics of rule-based GDA techniques across different domains. Such insights are instrumental in advancing research efforts to identify optimal data augmentation paths contingent on specific domain requirements. While learned GDA approaches are well-covered in other sections, the inclusion of edge addition strategies in Section 4.2 enriches the discourse, acknowledging an area that requires further exploration and debate due to the lack of consensus on its efficacy.

Additionally, virtual node additions, as recently investigated in the context of Graph Transformers, are now examined. This examination incorporates new virtual node methods into the tabular synthesis of techniques, enhancing the survey's comprehensiveness.

Finally, by reacting to the reviewers' suggestions to incorporate recent benchmarking work related to the graph out-of-distribution (OOD) problem, the revised paper establishes a definitive connection with contemporary research themes. Section 7.1's integration of this work signifies the authors' commitment to encapsulate relevant advancements across various domains in graph data processing.

Implications and Future Work

The revisions in this survey paper extend its relevance and utility in guiding future research within the domain of GDA. By clarifying methodological classifications and expanding on under-represented areas, the paper now acts as a critical touchstone for both practical and theoretical advances in the application of data augmentation in graph-based models.

Such integration of complex graph discussions not only highlights current challenges but also paves the way for advancing methodologies tailored to heterogeneous and dynamic networks. As the landscape of graph-based AI continues to evolve, understanding these complex interactions will be pivotal in the future development of AI-driven insights, models, and applications.

Overall, the revised survey serves as an enriched resource for researchers aiming to navigate the expanding domain of graph data augmentation. It lays a foundation upon which subsequent methodologies might build, fostering an informed trajectory towards more sophisticated and adaptable graph-based systems. The paper, therefore, is likely to serve as a significant contribution to ongoing research dialogues in the years to come.