Edge computing, which migrates compute-intensive tasks to run on the storage resources of edge devices, efficiently reduces data transmission loss and protects data privacy. However, due to limited computing resources and storage capacity, edge devices fail to support real-time streaming data query and processing. To address this challenge, first, we propose a Long Short-Term Memory (LSTM) network-based adaptive approach in the intelligent end-edge-cloud system. Specifically, we maximize the Quality of Experience (QoE) of users by automatically adapting their resource requirements to the storage capacity of edge devices through an event mechanism. Second, to reduce the uncertainty and non-complete adaption of the edge device towards the user’s requirements, we use the LSTM network to analyze the storage capacity of the edge device in real time. Finally, the storage features of the edge devices are aggregated to the cloud to re-evaluate the comprehensive capability of the edge devices and ensure the fast response of the user devices during the dynamic adaptation matching process. A series of experimental results show that the proposed approach has superior performance compared with traditional centralized and matrix decomposition based approaches.

This paper introduces a novel trust-aware hybrid recommendation framework that combines localitysensitive hashing (LSH) with the trust information in social networks, aiming to provide efficient and effective recommendations. Unlike traditional recommender systems which often overlook the critical influence of user trust, our proposed approach infuses trust metrics to better approximate user preferences. The LSH, with its intrinsic advantage in handling high-dimensional data and computational efficiency, is applied to expedite the process of finding similar items or users. We innovatively adapt LSH to form trust-aware buckets, encapsulating both trust and similarity information. These enhancements mitigate the sparsity and scalability issues usually found in existing recommender systems. Experimental results on a real-world dataset confirm the superiority of our approach in terms of recommendation quality and computational performance. The paper further discusses potential applications and future directions of the trust-aware hybrid recommendation with LSH.

Personalized recommendation plays a critical role in providing decision-making support for product and service analysis in the field of business intelligence. Recently, deep neural network-based sequential recommendation models gained considerable attention. However, existing approaches pay little attention to users’ dynamically evolving interests, which are influenced by product attributes, especially product category. To overcome these challenges, we propose a dynamic personalized recommendation model: DynaPR. Specifically, we first embed product information and attribute information into a unified data space. Then, we exploit long short-term memory (LSTM) networks to characterize sequential behavior over multiple time periods and seize evolving interests by hierarchical LSTM networks. Finally, similarity values between users are measured through pairwise interest features, and personalized recommendation lists are generated. A series of experiments reveal the superiority of the proposed method compared with other advanced methods.