Model extraction attack shows promising performance in revealing sequential recommendation (SeqRec) robustness, e.g., as an upstream task of transfer-based attack to provide optimization feedback for downstream attacks. However, existing work either heavily relies on impractical prior knowledge or has impressive attack performance. In this paper, we focus on data-free model extraction attack on SeqRec, which aims to efficiently train a surrogate model that closely imitates the target model in a practical setting. Conducting such an attack is challenging. First, imitating sequential training data for accurate model extraction is hard without prior knowledge. Second, limited queries for the target model require the attack to be efficient. To address these challenges, we propose a novel adversarial framework Sim4Rec which includes two modules, i.e., controllable sequence generation and reinforced adversarial distillation. The former allows a sequential generator to produce synthetic data similar to training data through pre-training with controllable generated samples. The latter efficiently extracts the target model via reinforced adversarial knowledge distillation. Extensive experiments demonstrate the advancement of Sim4Rec.

Session-based Recommendation (SBR) explores dynamic user interests based on short anonymous sessions. The performance of traditional ID-collaborative SBR models is constrained by the limited session length. To this end, we focus on Multi-modal Session-based Recommendation (MMSBR), which aims to leverage modality knowledge to promote short-term user interest modeling. Previous studies cannot solve the MMSBR problem well due to issues as: (I1) Modal-invariant and specific dependencies are difficult to disentangle and unify. (I2) Multiple modal transitions produce mutual effects. (I3) Modal-aware personalized preference discrepancy exists. Thus, we propose a modality-aware diffusion-based framework MDSD with subspace disentanglement, which decouples multi-modal collaborations and provides a new paradigm that integrates modality representation generation into next-item prediction. We first employ modality-disentangled consistency graphs based on affinity semantics to extract the unified modal subspace. Then we design a cross-modal contrastive attention fusion that explores cross-modality interplay, enhancing modal alignment and consistency. To interpret personalized interest discrepancy, we realize the preference-guided multi-modal diffusion, which combines modality-aware interest generation with prediction based on collaborative signals, thus providing comprehensive recommendation results. Extensive experiments on real-world datasets demonstrate the effectiveness of MDSD.

Industrial recommender systems usually train models incrementally to grasp recent interests of users. However, a fundamental issue of these incremental updated models is their tendency to overfit current data while neglecting past information. Specifically, we have observed that the data distribution of real systems exhibits periodic drifts, leading to periodic fluctuations of prediction bias. To alleviate the above bias fluctuations while minimizing the loss of recent interests, we propose TPIA, a Training-free approach for Periodic Interest Augmentation in incremental recommendation. Specifically, after the latest model is trained, we first calculate the importance score of each model in the previous period. Then, we merge these models based on the importance scores. To minimize information loss due to interference of parameters during model merging, we further develop a method for trimming redundant and abnormal parameters. Offline experiments on both public and private datasets demonstrate the effectiveness of TPIA. It has also been deployed on a large-scale industrial recommender system, and has shown a notable 1.61% increase in CVR and a 1.97% increase in CPM, along with enhanced stability in prediction bias.

Federated Recommendation (FedRec) has been widely applied recently for realizing privacy preservation in recommender systems. However, due to direct uploads of model gradients from all clients, FedRec is vulnerable to potential poisoning attacks. In this article, we focus on the targeted model poisoning attacks in FedRec, which aims to raise the exposure ratio of specific target items by generating poisoned gradients to influence global training. Challenges emerge when implementing this kind of attack. On the one hand, simulating authentic users on the malicious clients for downstream poisoning is hard when access to prior knowledge is limited. On the other hand, distinguished item attributes and personalized user preferences require the attack to be adaptive to complex distributions. To this end, we propose a novel attack DuAda with two modules, i.e., dummy user simulator and adaptive distribution attacker . The dummy user simulator is designed to generate malicious users with characteristics similar to real users, which exploits authentic user representations and preference labels simultaneously through two-stage inversion optimization. The attacker first extracts heterogeneous distributions by a special multi-prototype clustering method, and then conducts adaptive attacks from both explicit and implicit promotion perspectives. The explicit promotion raises the prediction scores of target items based on the inherent characteristics, while the implicit promotion imbues them with the features of popular items. Targeted at our proposed attack method, we also design a merged adaptive defense mechanism to fight against DuAda and conduct defensive experiments. Empirical studies on four real-world datasets demonstrate the effectiveness and interpretability of DuAda.