Zeyu Song

Yuecheng Li, Hengwei Ju, Zeyu Song et al.

Integrating large language model (LLM) representations into multimodal recommendation has shown promise, yet a fundamental challenge remains largely overlooked: the semantic heterogeneity between generative LM representations and the ID-based collaborative signals that recommendation systems rely on. Naively injecting LM features without alignment degrades recommendation performance rather than improving it. To resolve this, we propose RecGOAT, a dual-granularity semantic alignment framework built on graph neural networks and optimal transport theory. RecGOAT first enriches collaborative semantics through multimodal attentive graphs that capture item-item, user-item, and user-user relationships, initializing user representations via LLM-inferred behavioral preferences. It then aligns LM-derived modality representations with recommendation IDs at two complementary granularities: (1) instance-level alignment via cross-modal contrastive learning (CMCL), which produces discriminative per-sample representations; and (2) distribution-level alignment via optimal adaptive transport (OAT), which minimizes the 1-Wasserstein distance between ID distributions and LLM semantics to produce a unified, consistently aligned feature space. Theoretically, we prove that the unified representation achieves strictly lower target error than any single-modality representation, with the gap bounded by the Wasserstein distance and the InfoNCE loss, providing rigorous guarantees for both alignment consistency and fusion comprehensiveness. Extensive experiments on three public benchmarks demonstrate state-of-the-art performance. Deployment on a large-scale online advertising platform further validates RecGOAT's industrial scalability. Our code is available at https://github.com/6lyc/RecGOAT-LLM4Rec.

Ehsanul Kabir, Zeyu Song, Md Rafi Ur Rashid et al.

Federated learning (FL) is revolutionizing how we learn from data. With its growing popularity, it is now being used in many safety-critical domains such as autonomous vehicles and healthcare. Since thousands of participants can contribute in this collaborative setting, it is, however, challenging to ensure security and reliability of such systems. This highlights the need to design FL systems that are secure and robust against malicious participants' actions while also ensuring high utility, privacy of local data, and efficiency. In this paper, we propose a novel FL framework dubbed as FLShield that utilizes benign data from FL participants to validate the local models before taking them into account for generating the global model. This is in stark contrast with existing defenses relying on server's access to clean datasets -- an assumption often impractical in real-life scenarios and conflicting with the fundamentals of FL. We conduct extensive experiments to evaluate our FLShield framework in different settings and demonstrate its effectiveness in thwarting various types of poisoning and backdoor attacks including a defense-aware one. FLShield also preserves privacy of local data against gradient inversion attacks.

Yuecheng Li, Zeyu Song, Jing Yao et al.

Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry. However, aligning the LLM's semantic space with the recommender's ID space via post-training (e.g., SFT and RL) remains challenging. Existing LLM4Rec paradigms are bottlenecked by two main issues: (1) the difficulty of measuring and improving chain-of-thought (CoT) quality in open-domain recommendation during SFT, and (2) the neglect of the trade-off between LLM semantic rewards and recommendation preference rewards during RL alignment. Inspired by these challenges, we present Taiji, a novel LLM-as-Enhancer framework designed for industrial recommender systems. To overcome the SFT bottleneck, we utilize reverse-engineered reasoning and open-ended rejection sampling to generate high-quality, domain-specific CoT data. To resolve the RL alignment issue, we propose Pareto Optimal Policy Optimization (POPO), which adaptively adjusts cross-domain reward weights. Theoretically, it achieves an optimal trade-off between the semantic world knowledge of LLMs and the collaborative ID features representing online user preferences. Extensive offline evaluations and online A/B tests validate the effectiveness of Taiji. Deployed on Kuaishou's advertising platform since May 2026, Taiji currently serves over 400 million users daily, yielding significant commercial revenue and demonstrating its robust scalability in web-scale environments.

Zeyu Song, Ehsanul Kabir, Shagufta Mehnaz

Graph Neural Networks (GNNs) have become indispensable tools for learning from graph structured data, catering to various applications such as social network analysis and fraud detection for financial services. At the heart of these networks are the edges, which are crucial in guiding GNN models' predictions. In many scenarios, these edges represent sensitive information, such as personal associations or financial dealings, which require privacy assurance. However, their contributions to GNN model predictions may, in turn, be exploited by the adversary to compromise their privacy. Motivated by these conflicting requirements, this paper investigates edge privacy in contexts where adversaries possess only black-box access to the target GNN model, restricted further by access controls, preventing direct insights into arbitrary node outputs. Moreover, we are the first to extensively examine situations where the target graph continuously evolves, a common trait of many real-world graphs. In this setting, we present a range of attacks that leverage the message-passing mechanism of GNNs. We evaluated the effectiveness of our attacks using nine real-world datasets, encompassing both static and dynamic graphs, across four different GNN architectures. The results demonstrate that our attack outperforms existing methods across various GNN architectures, consistently achieving an F1 score of at least 0.8 in static scenarios. Furthermore, our attack retains robustness in dynamic graph scenarios, maintaining F1 scores up to 0.8, unlike previous methods that only achieve F1 scores around 0.2.

Jingtong Gao, Zeyu Song, Chi Lu et al.

Recommender systems are critical for delivering personalized content across digital platforms, and recent advances in Large Language Models (LLMs) offer new opportunities to enhance them with richer world knowledge and explicit reasoning capabilities. With the help of reasoning knowledge, recommendations can better infer users' underlying intents, adapt to evolving preferences, and leverage semantic relationships for improved accuracy and interpretability. However, existing reasoning-based recommendation methods often fail to fully align the LLM's reasoning process with recommendation-specific objectives due to structural disruption during integration and difficulties in translating free-form generation into accurate item predictions. In this paper, we introduce RPORec, a reinforced preference optimization framework that unifies an LLM backbone's reasoning ability with a dedicated recommendation head (Rechead) for precise item retrieval. RPORec comprises two stages: (1) Reasoning-Augmented Recommendation Modeling, where high-quality Chain-of-Thought (CoT) reasoning is generated and used as auxiliary knowledge to guide the Rechead in learning recommendation-specific representations; and (2) Advanced Reasoning Refinement and Alignment, in which the trained Rechead produces verifiable rewards to fine-tune the LLM backbone via reinforcement learning, enhancing reasoning quality, structural consistency, and task relevance. Extensive experiments on public benchmarks and large-scale online deployments show that RPORec consistently outperforms state-of-the-art LLM-based recommendation methods, demonstrating the effectiveness of reasoning-augmented recommendation modeling in real-world systems.

Zeyu Song, Sainyam Galhotra, Shagufta Mehnaz

The rise of distributed and privacy-preserving machine learning has sparked interest in decentralized gradient marketplaces, where participants trade intermediate artifacts like gradients. However, existing Federated Learning (FL) benchmarks overlook critical economic and systemic factors unique to such marketplaces-cost-effectiveness, fairness to sellers, and market stability-especially when a buyer relies on a private baseline dataset for evaluation. We introduce a comprehensive benchmark framework to holistically evaluate robust gradient aggregation methods within these buyer-baseline-reliant marketplaces. Our contributions include: (1) a simulation environment modeling marketplace dynamics with a variable buyer baseline and diverse seller distributions; (2) an evaluation methodology augmenting standard FL metrics with marketplace-centric dimensions such as Economic Efficiency, Fairness, and Selection Dynamics; (3) an in-depth empirical analysis of the existing Distributed Gradient Marketplace framework, MartFL, including the integration and comparative evaluation of adapted FLTrust and SkyMask as alternative aggregation strategies within it. This benchmark spans diverse datasets, local attacks, and Sybil attacks targeting the marketplace selection process; and (4) actionable insights into the trade-offs between model performance, robustness, cost, fairness, and stability. This benchmark equips the community with essential tools and empirical evidence to evaluate and design more robust, equitable, and economically viable decentralized gradient marketplaces.