Pre-Conference Talk by LIU Zhongzhou and WEN Zhihao

Talk #1: Mitigating Popularity Bias for Users and Items with Fairness-centric Adaptive Recommendation
by LIU Zhongzhou, PhD Candidate

Recommendation systems are popular in many domains. Researchers usually focus on the effectiveness of recommendation (e.g., precision) but neglect the popularity bias that may affect the fairness of the recommendation, which is also an important consideration that could influence the benefits of users and item providers. A few studies have been proposed to deal with the popularity bias, but they often face two limitations. Firstly, most studies only consider fairness for one side—either users or items, without achieving fairness jointly for both. Secondly, existing methods are not sufficiently tailored to each individual user or item to cope with the varying extent and nature of popularity bias. To alleviate these limitations, in this paper, we propose FAiR, a fairness-centric model that adaptively mitigates the popularity bias in both users and items for recommendation. Concretely, we design explicit fairness discriminators to mitigate the popularity bias for each user and item locally, and an implicit discriminator to preserve fairness globally. Moreover, we dynamically adapt the model to different input users and items to handle the differences in their popularity bias. Finally, we conduct extensive experiments to demonstrate that our model significantly outperforms state-of-the-art baselines in fairness metrics, while remaining competitive in effectiveness.

Talk #2: Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Prompting  
by WEN Zhihao, PhD Candidate

Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions. However, low-resource text classification, with few or no labeled samples, poses a serious concern for supervised learning. Meanwhile, many text data are inherently grounded on a network structure, such as a hyperlink/citation network for online articles, and a user-item purchase network for e-commerce products. These graph structures capture rich semantic relationships, which can potentially augment low-resource text classification. In this paper, we propose a novel model called Graph-Grounded Pre-training and Prompting (G2P2) to address low-resource text classification in a two-pronged approach. During pre-training, we propose three graph interaction-based contrastive strategies to jointly pre-train a graph-text model; during downstream classification, we explore prompting for the jointly pre-trained model to achieve low-resource classification. Extensive experiments on four real-world datasets demonstrate the strength of G2P2 in zero- and few-shot low-resource text classification tasks.

LIU Zhongzhou is a Ph.D. candidate in Computer Science at the SMU School of Computing and Information Systems, supervised by Assistant Prof. FANG Yuan. His research aims to investigate the untapped possibilities of recommendation systems that go beyond traditional user-item collaborative filtering, including fine-grained preferences, fairness, causality-based recommendations and more.

Zhihao Wen is a PhD candidate in Computer Science at Singapore Management University, supervised by Prof. FANG Yuan. Zhihao Wen conducts research on graph neural networks (GNNs) and text mining. In particular, he expresses interest in enhancing the generalizability of Graph Neural Networks (GNNs), improving the performance of GNNs under low-resource conditions, and exploring the application of GNNs in text mining tasks. His first-authored papers are published on top venues, including WWW, SIGIR, WSDM, etc. Before joining SMU, Zhihao obtained a dual Bachelor's degree in Electronic Engineering and E-commerce from the University of Electronic Science and Technology of China.