Natural language processing (NLP) assists to increase the efficiency of human and Multimedia Internet of Things (MIoT) interaction. Notably, large-scale NLP tasks can be offloaded from a cloud server to fog nodes clos...
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Natural language processing (NLP) assists to increase the efficiency of human and Multimedia Internet of Things (MIoT) interaction. Notably, large-scale NLP tasks can be offloaded from a cloud server to fog nodes closer to a mobile terminal device for lower response latency. But communication security is ongoing issues that need to be addressed. Effective mutual authentication among multiple entities is essential to ensure the security of MIoT systems based on a dynamic Fog computing Network (FCN). However, the existing schemes are unsuitable for the dynamic FCN due to the security vulnerabilities such as the linkable sessions. To solve this problem, an Anonymous Multi-Party Authentication (AMPA) scheme is proposed to address the challenges of secure FCN-based MIoT communications in this paper. The proposed scheme uses a bilinear pairing operation to realize the authentication between the fog nodes and cloud server and to establish the group key. Besides, the scheme allows cloud-authenticated terminal devices to be added to the FCN and reduces the need for the resource-limited terminal device to perform many authentication protocols. The security analysis is carried out to demonstrate that AMPA scheme can meet various safety requirements. Performance evaluations shown that the proposed AMPA scheme achieves satisfactory performance.
Inferring user preferences from users’ historical feedback is a valuable problem in recommender systems. Conventional approaches often rely on the assumption that user preferences in the feedback data are equivalent ...
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Inferring user preferences from users’ historical feedback is a valuable problem in recommender systems. Conventional approaches often rely on the assumption that user preferences in the feedback data are equivalent to the real user preferences without additional noise, which simplifies the problem modeling. However, there are various confounders during user-item interactions, such as weather and even the recommendation system itself. Therefore, neglecting the influence of confounders will result in inaccurate user preferences and suboptimal performance of the model. Furthermore, the unobservability of confounders poses a challenge in further addressing the problem. Along these lines, we refine the problem and propose a more rational solution to mitigate the influence of unobserved confounders. Specifically, we consider the influence of unobserved confounders, disentangle them from user preferences in the latent space, and employ causal graphs to model their interdependencies without specific labels. By ingeniously combining local and global causal graphs, we capture the user-specific effects of confounders on user preferences. Finally, we propose our model based on Variational Autoencoders, named Causal Structure Aware Variational Autoencoders (CSA-VAE) and theoretically demonstrate the identifiability of the obtained causal graph. We conducted extensive experiments on one synthetic dataset and nine real-world datasets with different scales, including three unbiased datasets and six normal datasets, where the average performance boost against several state-of-the-art baselines achieves up to 9.55%, demonstrating the superiority of our model. Furthermore, users can control their recommendation list by manipulating the learned causal representations of confounders, generating potentially more diverse recommendation results. Our code is available at Code-link.
Non-overlapping Cross-domain Sequential Recommendation (NCSR) is the task that focuses on domain knowledge transfer without overlapping entities. Compared with traditional Cross-domain Sequential Recommendation (CSR),...
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Non-overlapping Cross-domain Sequential Recommendation (NCSR) is the task that focuses on domain knowledge transfer without overlapping entities. Compared with traditional Cross-domain Sequential Recommendation (CSR), NCSR poses several challenges: 1) NCSR methods often rely on explicit item IDs, overlooking semantic information among entities. 2) Existing CSR mainly relies on domain alignment for knowledge transfer, risking semantic loss during alignment. 3) Most previous studies do not consider the many-to-one characteristic, which is challenging because of the utilization of multiple source domains. Given the above challenges, we introduce the prompt learning technique for Many-to-one Non-overlapping Cross-domain Sequential Recommendation (MNCSR) and propose a Text-enhanced Co-attention Prompt Learning Paradigm (TCPLP). Specifically, we capture semantic meanings by representing items through text rather than IDs, leveraging natural language universality to facilitate cross-domain knowledge transfer. Unlike prior works that need to conduct domain alignment, we directly learn transferable domain information, where two types of prompts, i.e., domain-shared and domain-specific prompts, are devised, with a co-attention-based network for prompt encoding. Then, we develop a two-stage learning strategy, i.e., pre-train & prompt-tuning paradigm, for domain knowledge pre-learning and transferring, respectively. We conduct extensive experiments on three datasets and the experimental results demonstrate the superiority of our TCPLP. Our source codes have been publicly released.
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