As market competition intensifies and workforce scales grow, organizations increasingly seek precision in aligning role specifications with talent acquisition strategies. Employee-job alignment is a crucial aspect of strategic human resource management (HRM). Traditional methods, however, fall short in handling multi-criteria employee recommendations, adapting to dynamic preferences, and mitigating systemic biases. This study introduces a large language model (LLM)-enhanced framework that integrates dynamic hierarchical analysis with talent data retention mechanisms. A dynamic priority weight pool, based on traditional hierarchical analysis and utilizing text similarity matching, enables the LLM to efficiently access the importance matrix of skills required by the hierarchical analysis method. Simultaneously, the introduction of a bias-correction memory pool and preference aggregation pool enables the persistent storage of error correction memory and result expression bias. Leveraging the Ebbinghaus forgetting curve, a dynamic forgetting mechanism and a preference strength evaluation mechanism are developed to assess memory and preference strength, automatically eliminating redundant error correction and preference data to prevent data accumulation. Empirical evaluations show that this framework improves talent recommendation accuracy, customizes result presentation to align with institutional preferences, and reduces selection biases compared to traditional HRM systems, effectively tackling the complexities of modern workforce management.

With the popularity and development of smartphones, many mobile applications of various types have emerged. How to recommend mobile applications that match the user’s preferences and usage habits among the massive applications is a problem that needs to be solved. Traditional mobile application recommendation methods cannot dynamically track user behavior and preference changes in time and cannot timely correct the recommendation model, resulting in poor recommendation effects. The continual update of mobile applications will also invalidate the recommendation model based on traffic classification. To solve these problems, this article proposes A Deep Q-Network– (DQN) based traffic classification method for mobile application recommendation with continual learning, which embeds a DQN-based traffic classification model in the mobile terminal and sets up a reward and punishment mechanism to achieve self-supervised learning. By continuously adjusting and optimizing the model, the effectiveness of the traffic classification model is ensured, and the recommendation model is provided with accurate and reliable user behavior data support. Experiments on the ISCX and private datasets show that the proposed method performs better and can effectively guarantee the accuracy of the classification model.

Authentication is crucial for network system security, relying on methods such as passwords, ID cards, biometrics, and behavioral characteristics. The conventional centralized authentication may lead to potential performance bottlenecks and privacy risks such as key exposure, single point of failure. Decentralized authentication systems using cryptographic techniques aim to address these issues but often tradeoff between flexibility and communication efficiency. In this paper we propose a new cryptographic concept called designated private set-based trapdoor authentication (DPSBTA) for flexible and efficient trust management in decentralized systems. DPSBTA eliminates the need for a trusted authority, with users’ access privileges defined by their private sets. During the authentication process, each server can designate an element set and only if a user holds adequate elements which are contained in the designated set can he obtains a credential from the server. The key features of DPSBTA include: decentralized trapdoor authentication management, without a trusted authority, conducted in a double threshold manner; privacy preservation, as servers do not know users’ element holdings or credential generation; round-optimal communication, with only two rounds of interaction between users and servers. We present the generic construction, security models, and concrete algorithms with correctness proof. The theoretical proof and the performance evaluations demonstrate the tangible security and high efficacy of the proposed DPSBTA.