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Media recommender systems aim to capture users’ preferences and provide precise personalized recommendation of media content. There are two critical components in the common paradigm of modern recommender models: (1) representation learning, which generates an embedding for each user and item; and (2) interaction modeling, which fits user preferences toward items based on their representations. In spite of great success, when a great amount of users and items exist, it usually needs to create, store, and optimize a huge embedding table, where the scale of model parameters easily reach millions or even larger. Hence, it naturally raises questions about the heavy recommender models: Do we really need such large‐scale parameters? We get inspirations from the recently proposed lottery ticket hypothesis (LTH), which argues that the dense and over‐parameterized model contains a much smaller and sparser sub‐model that can reach comparable performance to the full model. In this paper, we extend LTH to media recommender systems, aiming to find the winning tickets in deep recommender models. To the best of our knowledge, this is the first work to study LTH in media recommender systems. With Matrix Factorization and Light Graph Convolution Networks as the backbone models, we found that there widely exist winning tickets in recommender models. On three media convergence data sets—Yelp2018, TikTok and Kwai, the winning tickets can achieve comparable recommendation performance with only 29 % ~ 48 % , 7 % ~ 10 %, and 3 % ~ 17 % of parameters, respectively.

With the greater emphasis on privacy and security in our society, the problem of graph unlearning -revoking the influence of specific data on the trained GNN model, is drawing increasing attention. However, ranging from machine unlearning to recently emerged graph unlearning methods, existing efforts either resort to retraining paradigm, or perform approximate erasure that fails to consider the inter-dependency between connected neighbors or imposes constraints on GNN structure, therefore hard to achieve satisfying performance-complexity trade-offs.In this work, we explore the influence function tailored for graph unlearning, so as to improve the unlearning efficacy and efficiency for graph unlearning. We first present a unified problem formulation of diverse graph unlearning tasks w.r.t. node, edge, and feature. Then, we recognize the crux to the inability of traditional influence function for graph unlearning, and devise Graph Influence Function (GIF), a model-agnostic unlearning method that can efficiently and accurately estimate parameter changes in response to a 𝜖-mass perturbation in deleted data. The idea is to supplement the objective of the traditional influence function with an additional loss term of the influenced neighbors due to the structural dependency. Further deductions on the closed-form solution of parameter changes provide a better understanding of the unlearning mechanism. We conduct extensive experiments on four representative GNN models and three benchmark datasets to justify the superiority of GIF for diverse graph unlearning tasks in terms of unlearning efficacy, model utility, and unlearning efficiency. Our implementations are available at https://github.com/wujcan/GIF-torch/.

Out-of-distribution (OOD) generalization on graphs is drawing widespread attention. However, existing efforts mainly focus on the OOD issue of correlation shift. While another type, covariate shift, remains largely unexplored but is the focus of this work. From a data generation view, causal features are stable substructures in data, which play key roles in OOD generalization. While their complementary parts, environments, are unstable features that often lead to various distribution shifts. Correlation shift establishes spurious statistical correlations between environments and labels. In contrast, covariate shift means that there exist unseen environmental features in test data. Existing strategies of graph invariant learning and data augmentation suffer from limited environments or unstable causal features, which greatly limits their generalization ability on covariate shift. In view of that, we propose a novel graph augmentation strategy: Adversarial Causal Augmentation (AdvCA), to alleviate the covariate shift. Specifically, it adversarially augments the data to explore diverse distributions of the environments. Meanwhile, it keeps the causal features invariant across diverse environments. It maintains the environmental diversity while ensuring the invariance of the causal features, thereby effectively alleviating the covariate shift. Extensive experimental results with in-depth analyses demonstrate that AdvCA can outperform 14 baselines on synthetic and real-world datasets with various covariate shifts.