Large language model-powered sequential recommender systems (LLM-SRSs) have recently demonstrated remarkable performance, enabling recommendations through prompt-driven inference over user interaction sequences. However, this paradigm also introduces new security vulnerabilities, particularly text-level manipulations, rendering them appealing targets for promotion attacks that purposely boost the ranking of specific target items. Although such security risks have been receiving increasing attention, existing studies typically rely on an unrealistic assumption of access to either the victim model or prompt to unveil attack mechanisms. In this work, we investigate the item promotion attack in LLM-SRSs under a more realistic setting where both the system prompt and victim model are unknown to the attacker, and propose a Prompt-Unknown Dual-poisoning Attack (PUDA) framework. To simulate attacks under this full black-box setting, we introduce an LLMbased evolutionary refinement strategy that infers discrete system prompts, enabling the training of an effective surrogate model that mimics the behaviors of the victim model. Leveraging the distilled prompt and surrogate model, we devise a promotion attack that adversarially revises target item texts under semantic constraints, which is further complemented by the highly plausible, surrogategenerated poisoning sequences to enable cost-effective target item promotion. Extensive experiments on real-world datasets demonstrate that PUDA consistently outperforms state-of-the-art competitors in boosting the exposure of unpopular target items. Our findings reveal critical security risks in modern LLM-SRSs even when both prompts and models are protected, and highlight the need for more robust defensive means.

Spatial transcriptomics (ST) enables spatially resolved gene expression profiling within intact tissue sections. However, its widespread adoption is constrained by the high cost and low throughput of current sequencing-based protocols. This has motivated growing interest in computationally predicting gene expression directly from routinely acquired histology images. Existing methods are largely restricted to isolated 2D tissue slices and fail to capture richer spatial relationships or structured dependencies among spot-level gene expression profiles. In this paper, we propose STAG, a dual-branch framework for gene-aware expression prediction and spatial context modeling. A Query branch predicts ST expression for an individual target spot, while a Neighbor branch acts as an auxiliary branch to model structured relationships among multiple spots. By leveraging hypergraph learning, the Neighbor branch captures higher-order spatial and molecular dependencies, enabling unified modeling of both intra-slice and inter-slice relationships. This design supports standard 2D settings (a single slice) and naturally extends to 3D scenarios when adjacent tissue sections are available. Moreover, STAG leverages gene semantic information as biological guidance by encoding gene names with a foundation model, enabling coordinated gene-aware interactions beyond independent gene prediction. STAG achieves an average gain of 5.16% in PCC@250 across six datasets. Under highly variable gene selection, STAG maintains the lowest RMSE and highest PCC@50 across three datasets. The effectiveness of the learned representations is further demonstrated in pseudo-3D prediction and downstream cancer classification tasks. Code is available at https://github.com/MCPathology/STAG.

Sequential recommender systems (SRSs) excel in capturing users' dynamic interests, thus playing a key role in various industrial applications. The popularity of SRSs has also driven emerging research on their security aspects, where data poisoning attack for targeted item promotion is a typical example. Existing attack mechanisms primarily focus on increasing the ranks of target items in the recommendation list by injecting carefully crafted interactions (i.e., poisoning sequences), which comes at the cost of demoting users' real preferences. Consequently, noticeable recommendation accuracy drops are observed, restricting the stealthiness of the attack. Additionally, the generated poisoning sequences are prone to substantial repetition of target items, which is a result of the unitary objective of boosting their overall exposure and lack of effective diversity regularizations. Such homogeneity not only compromises the authenticity of these sequences, but also limits the attack effectiveness, as it ignores the opportunity to establish sequential dependencies between the target and many more items in the SRS. To address the issues outlined, we propose a Diversity-aware Dual-promotion Sequential Poisoning attack method named DDSP for SRSs. Specifically, by theoretically revealing the conflict between recommendation and existing attack objectives, we design a revamped attack objective that promotes the target item while maintaining the relevance of preferred items in a user's ranking list. We further develop a diversity-aware, auto-regressive poisoning sequence generator, where a re-ranking method is in place to sequentially pick the optimal items by integrating diversity constraints. By attacking two representative SRSs on three real-world datasets, comprehensive experimental results demonstrate that DDSP outperforms state-of-the-art attack methods in attack effectiveness. Moreover, DDSP achieves the strongest stealthiness with its lowest impact on recommendation accuracy.

Conventional cross-domain recommendation models, which require centrally collecting varieties of original data from users, usually meet a challenge that users are reluctant to provide their real feedbacks because of privacy concerns. Thus, federated learning is incorporated into cross-domain recommendation, since it aggregates parameters of local models trained on user sides to train a global recommendation model, instead of centralized data collection. However, the deviations between the global model and local ones, which are caused by users’ data with non-independent and identical distributions, significantly challenge existing federated learning-based models in terms of alleviating data sparsity and cold-start problems. This article proposes a novel end-to-end federated contrastive learning-based model towards cross-domain recommendation, namely${{Fed-CLR}}$. It first uses an inference model to characterize interaction distributions of users in source domain(s), then reconstructs historical interactions of users in target domain(s) with a generative model, and finally performs federated contrastive learning at model level (including inner-model and inter-model) to help reduce deviations between the global model and local ones. Particularly, a constraint mechanism, namely${{Con-Mec}}$, is proposed to achieve consistency reinforcement from the aspect of inner- and inter-models. The experimental results on three real-world datasets not only show that${{Fed-CLR}}$outperforms the state-of-the-art peers, but also demonstrate that${{Fed-CLR}}$achieves a faster convergence speed than classical federated learning-based models.

As a new recommendation pattern combining collaborative filtering and social network, social recommender system strives to introduce auxiliary user relations to alleviate data sparsity problems. Considering the graph structure characteristics of user historical interactions and social network, there have been emerged several innovative works that utilize Graph Neural Network (GNN) for social recommendation to show impressive performance. However, existing works seem to be restricted to exploiting social network as auxiliary information for main recommendation tasks, with little attention on the social network itself at the fine-grained level. From empirical perspective, the effectiveness of directly applying social network to social recommendation via GNNs may be limited since the social information that can be used for training is actually sparser than user interactions, and most of observable social information is not valid. To resolve this problem, we propose a Dual Variational Graph Reconstruction Learning (DVGRL) framework for social recommendation. It treats user interaction graph and social network as equivalent and aims to learn both variational distributions of user preferences from historical interactions and social connections, which are trained simultaneously and used to guide the reconstruction of historical interaction graph and social network. To effectively exploit the social information gleaned from reconstruction learning for enhancing recommendation, we design two inter-domain fusion mechanisms to achieve knowledge transfer from the perspectives of attention features and prior distributions, respectively. Extensive experiments on four real-world datasets validate the effectiveness of DVGRL for social recommendation tasks.