Out-of-distribution (OOD) graph generalization are critical for many real-world applications. Existing methods neglect to discard spurious or noisy features of inputs, which are irrelevant to the label. Besides, they mainly conduct instance-level class-invariant graph learning and fail to utilize the structural class relationships between graph instances. In this work, we endeavor to address these issues in a unified framework, dubbed I ndividual and S tructural G raph I nformation B ottlenecks ( IS-GIB ). To remove class spurious feature caused by distribution shifts, we propose Individual Graph Information Bottleneck (I-GIB) which discards irrelevant information by minimizing the mutual information between the input graph and its embeddings. To leverage the structural intra- and inter-domain correlations, we propose Structural Graph Information Bottleneck (S-GIB). Specifically for a batch of graphs with multiple domains, S-GIB first computes the pair-wise input-input, embedding-embedding, and label-label correlations. Then it minimizes the mutual information between input graph and embedding pairs while maximizing the mutual information between embedding and label pairs. The critical insight of S-GIB is to simultaneously discard spurious features and learn invariant features from a high-order perspective by maintaining class relationships under multiple distributional shifts. Notably, we unify the proposed I-GIB and S-GIB to form our complementary framework IS-GIB. Extensive experiments conducted on both node- and graph-level tasks consistently demonstrate the superior generalization ability of IS-GIB. The code is available at https://github.com/YangLing0818/GraphOOD .

In modern commercial search engines and recommendation systems, data from multiple domains is available to jointly train the multi-domain model. Traditional methods train multi-domain models in the multi-task setting, with shared parameters to learn the similarity of multiple tasks, and task-specific parameters to learn the divergence of features, labels, and sample distributions of individual tasks. With the development of large language models, LLM can extract global domain-invariant text features that serve both search and recommendation tasks. We propose a novel framework called S&R Multi-Domain Foundation, which uses LLM to extract domain invariant features, and Aspect Gating Fusion to merge the ID feature, domain invariant text features and task-specific heterogeneous sparse features to obtain the representations of query and item. Additionally, samples from multiple search and recommendation scenarios are trained jointly with Domain Adaptive Multi-Task module to obtain the multi-domain foundation model. We apply the S&R Multi-Domain foundation model to cold start scenarios in the pretrain-finetune manner, which achieves better performance than other SOTA transfer learning methods. The S&R Multi-Domain Foundation model has been successfully deployed in Alipay Mobile Application's online services, such as content query recommendation and service card recommendation, etc.

Graph generation is a fundamental task in machine learning with broad impacts on numerous real-world applications such as biomedical discovery and social science. Most recently, generative models, especially diffusion models (DMs), have shown great promise in synthesizing realistic graphs. However, existing DMs methods typically conduct diffusion processes directly in complex graph space (i.e., node feature, adjacency matrix, or both), resulting in high modeling complexity and poor multimodal distribution coverage. In this paper, we propose Graphusion, a novel and unified latent-based graph generative framework to address the problems. Specifically, Graphusion is composed of a variational graph autoencoder mapping raw graphs with high-dimensional discrete space to low-dimensional topology-injected latent space, and latent DMs running there, producing a smoother, faster, and more expressive graph generation procedure. Thanks to the latest space modeling, we further develop principled latent self-guidance to sufficiently cover the whole semantical distribution of the unlabeled graph set. Experiments show that our Graphusion framework can consistently outperform previous graph generation baselines on both generic and molecular graph datasets, demonstrating the generality and extensibility along with further analytical justifications.

In recent years, the explosion of web videos makes text-video retrieval increasingly essential and popular for video filtering, recommendation, and search. Text-video retrieval aims to rank relevant text/video higher than irrelevant ones. The core of this task is to precisely measure the cross-modal similarity between texts and videos. Recently, contrastive learning methods have shown promising results for text-video retrieval, most of which focus on the construction of positive and negative pairs to learn text and video representations. Nevertheless, they do not pay enough attention to hard negative pairs and lack the ability to model different levels of semantic similarity. To address these two issues, this paper improves contrastive learning using two novel techniques. First, to exploit hard examples for robust discriminative power, we propose a novel Dual-Modal Attention-Enhanced Module (DMAE) to mine hard negative pairs from textual and visual clues. By further introducing a Negative-aware InfoNCE (NegNCE) loss, we are able to adaptively identify all these hard negatives and explicitly highlight their impacts in the training loss. Second, our work argues that triplet samples can better model fine-grained semantic similarity compared to pairwise samples. We thereby present a new Triplet Partial Margin Contrastive Learning (TPM-CL) module to construct partial order triplet samples by automatically generating fine-grained hard negatives for matched text-video pairs. The proposed TPM-CL designs an adaptive token masking strategy with cross-modal interaction to model subtle semantic differences. Extensive experiments demonstrate that the proposed approach outperforms existing methods on four widely-used text-video retrieval datasets, including MSR-VTT, MSVD, DiDeMo and ActivityNet.

At present, people are gradually becoming aware of financial management and thus fund recommendation attracts more and more attention to help them find suitable funds quickly. As a user usually takes many factors (e.g., fund theme, fund manager) into account when investing a fund and the fund usually consists of a substantial collection of investments, effectively modeling multi-interest representations is more crucial for personalized fund recommendation than the traditional goods recommendation. However, existing multi-interest methods are largely sub-optimal for fund recommendation, since they ignore financial domain knowledge and diverse fund investment intentions. In this work, we propose a Disentangled Interest importance aware Knowledge Graph Neural Network (DIKGNN) for personalized fund recommendation on FinTech platforms. In particular, we restrict the multiple intent spaces by introducing the attribute nodes from the fund knowledge graph as the minimum intent modeling unit to utilize financial domain knowledge and provide interpretability. In the intent space, we define disentangled intent representations, equipped with intent importance distributions to describe the diverse fund investment intentions. Then we design a new neighbor aggregation mechanism with the learned intent importance distribution upon the interaction graph and knowledge graph to collect multi-intent information. Furthermore, we leverage micro independence and macro balance constraints on the representations and distributions respectively to encourage intent independence and diversity. The extensive experiments on public recommendation benchmarks demonstrate that DIKGNN can achieve substantial improvement over state-of-the-art methods. Our proposed model is also evaluated over one real-world industrial fund dataset from a FinTech platform and has been deployed online.

Relevance modeling aims to locate desirable items for corresponding queries, which is crucial for search engines to ensure user experience. Although most conventional approaches address this problem by assessing the semantic similarity between the query and item, pure semantic matching is not everything. In reality, auxiliary query-item interactions extracted from user historical behavior data of the search log could provide hints to reveal users' search intents further. Drawing inspiration from this, we devise a novel Behavior Augmented Relevance Learning model for Alipay Search (BARL-ASe) that leverages neighbor queries of target item and neighbor items of target query to complement target query-item semantic matching. Specifically, our model builds multi-level co-attention for distilling coarse-grained and fine-grained semantic representations from both neighbor and target views. The model subsequently employs neighbor-target self-supervised learning to improve the accuracy and robustness of BARL-ASe by strengthening representation and logit learning. Furthermore, we discuss how to deal with the long-tail query-item matching of the mini apps search scenario of Alipay practically. Experiments on real-world industry data and online A/B testing demonstrate our proposal achieves promising performance with low latency.