Shared-account usage is common on short-video platforms, especially on mobile and tablet devices, where a single device is accessed by multiple users. While existing industrial solutions generally focus on behavior sequence purification to disentangle mixed user preferences, such approaches inherently depend on behavior accumulation and therefore lack the capability for real-time user identification. To adapt to online recommendation, utilizing gesture interaction features is a natural and promising option, as they (1) are instantaneous without behavior collection and (2) naturally encode fine-grained user operation habits. Nevertheless, we empirically observe that directly incorporating raw gesture features into recommendation models yields limited gains. Identity-discriminative patterns embedded in gesture signals are largely entangled during the main model training, preventing them from being leveraged as explicit and reliable identity cues. As a result, efficiently utilizing gesture information to provide more distinct identity signals for recommendation models remains a critical challenge. To address this issue, we propose G-CORE (Gesture Clustering for Real-time REcommendation), an unsupervised framework that disentangles gesture representations via clustering before integrating them into the main recommendation model. By providing clearer and more identity-aware signals, G-CORE enables the main model with faster user switching without relying on a volume of behavior accumulation. Through extensive offline experiments and online A/B tests on Kuaishou platform, G-CORE demonstrates its effectiveness in various shared-account scenarios, and has been successfully deployed in the Mobile and Tablet system of the platform.

Despite the success of Graph Neural Networks (GNNs) in modeling recommender systems as bipartite graphs, their ability to capture diverse user-item relations remains limited by the sparsity of observed interactions, which fails to reveal the underlying latent intents. We propose IACLR (Intention Alignment via Contrastive Learning for bipartite graph Recommendation), a framework that constructs an Intent-Graph by augmenting the bipartite recommendation graphs with an implicit intent layer. Instead of relying solely on observed edges, IACLR introduces a set of intent nodes that bridge users and items through shared semantic and behavioral patterns. These nodes are used to construct an Intent-Graph, where they act as both intermediaries that enrich structural connectivity and global anchors that summarize latent user interests. Within this graph, IACLR performs contrastive alignment across multiple data views and enforces consistency among users, intents, and items, thereby enhancing robustness under data sparsity. Experiments on benchmark datasets (e.g., Amazon-books and Yelp) demonstrate that IACLR consistently outperforms strong graph-based, revealing its effectiveness in capturing fine-grained user–item relationships and integrating multi-faceted signals. The framework is applicable to various recommendation scenarios, including academic paper recommendations, e-commerce, and content platforms.

Graph neural networks (GNNs) are widely used in information retrieval, but they often require large amounts of labeled data. To address this problem, self-supervised methods like graph contrastive learning (GCL) are developed to learn from graph structures without labeled data. However, GCL faces a challenge in practice. Plenty of traditional GCL methods differ fundamentally from GNNs in handling neighboring nodes, hindering effective contrastive learning. To address the above issue, we propose a graph self-supervised learning model based on Contrastive Graph Matching (ConGM). The model effectively mitigates the conflict between GCL methods and the homophily assumption of GNNs by using linear node matching and quadratic edge alignment mechanisms to treat some neighboring nodes as both positive and negative samples, rather than considering all neighboring nodes as negative samples as in traditional GCL methods. Additionally, to tackle the imbalance of positive and negative samples in edge alignment, we design a bi-level negative sample selection strategy to choose appropriate hard negative samples. Extensive experiments conducted on multiple benchmark datasets have validated the effectiveness of our proposed method.

Link sign prediction in signed bipartite graphs, which are extensively utilized across diverse domains such as social networks and recommendation systems, has recently emerged as a pivotal challenge. However, significant space and time complexities associated with the scalability of bipartite graphs pose substantial challenges, particularly in large-scale environments. To address these issues, this paper introduces the SignFlow Bipartite Subgraph Network (SBSN), balancing sublinear training memory growth through a heuristic subgraph extraction method integrated with a novel message passing module, with optimal inference efficiency achieved via the node feature distillation module. Our subgraph sampling approach reduces the graph size by focusing on neighborhoods around target links and employs an optimized directed message passing mechanism to aggregate critical structural patterns. This mechanism allows SBSN to efficiently learn rich local structural patterns essential for accurate sign prediction. Furthermore, to overcome the inefficiency of subgraph sampling-based models during inference, SBSN incorporates a node feature distillation module after the first training stage. This module distills subgraph features into node features, enabling fast inference while retaining the rich structural information of subgraphs. Experiments reveal that SBSN shows superior performance in both medium- and large-scale datasets, efficiently managing memory and computational resources, making it a scalable solution for extensive applications.

Signed bipartite graphs are widely employed in social networks, recommender systems, and other domains, where the nodes represent two different sets, such as users and commodities, and the links have positive and negative signs to reflect the ratings. Link sign prediction is a crucial task, but the link class imbalance when one type of link (e.g., the head class) is significantly more numerous than another (e.g., the tail class) makes this task extremely challenging. To address this challenge, we propose a Line-Graph-Based Dynamic Balancing Prediction (LDBP) method. Specifically, we first convert the links of a signed bipartite graph into nodes of a line graph, and then the link class imbalance problem in the bipartite graph is naturally transformed to the problem of node class imbalance in the line graph. To tackle this problem, we introduce a Centroid Contrastive Learning (CCL) method and design a Dynamic Synthesis & Deletion (DSD) strategy for the tail-class nodes. By dynamically adjusting the quantity of synthesized tail-class nodes, we obtain a Balanced Line Graph (BaLG). Extensive experiments on real-world datasets demonstrate the effectiveness of our method in improving the accuracy of link sign prediction and addressing the issue of link class imbalance.

Personalized news recommendation plays a vital role in mitigating information overload, yet challenges persist in accurately capturing user preferences and fine-grained interests. Leveraging the semantic understanding and extraction capabilities of large language models (LLMs), we propose a Multi-Interest Personalized News Recommendation (MIPNR) model to address these issues. MIPNR separately models user interests at the user, news, and entity levels. Specifically, we introduce a Category-Guided Interest-News Matching (CGIN-Matching) method to identify potential interests, a Local News Entity Graph (LNEG) to model subtle entity relationships, and an entity-wise attention mechanism to extract fine-grained interests. In addition, LLMs are used to generate explicit textual descriptions of user preferences. Extensive experiments on real-world datasets demonstrate the effectiveness of our approach.

In the field of graph representation learning for molecular property prediction, self-supervised learning is highly appreciated for its ability to mine potential connections in unlabeled data. However, existing approaches such as neighborhood prediction or contrastive learning often lack in fusing structural and semantic features of molecular graphs. To address this issue, we propose a Graph Matching Based Graph Self-Supervised Learning (GMSSL) framework, which aims to comprehensively learn the structure and semantics of molecular graphs. Specifically, utilizing intra-graph and inter-graph convolution and graph matching methods, GMSSL is able to encode complex information for efficient training without supervision. After pre-training and fine-tuning, GMSSL demonstrates powerful molecular property prediction on multiple datasets.