Research on intelligent recommendation technology for enterprise mobile office based on LSTM network

In the context of the multitude of functionalities present within enterprise mobile office platforms and the laborious process users face in locating and utilizing the specific features they require, this paper introduces an intelligent recommendation methodology tailored for enterprise mobile office platform functions, underpinned by Long Short-Term Memory (LSTM) networks. Initially, an extensive corpus of user behavioral data and functional metadata from the enterprise mobile office platform is amassed. Subsequently, an LSTM-based intelligent recommendation model is formulated. This model commences by extracting behavioral sequences from the platform's historical usage data, subsequently employing the LSTM network to model the users' historical interactions. Through rigorous training, the LSTM network adeptly discerns and assimilates the behavioral patterns and preferences of users, thereby enabling the generation of tailored recommend dation outcomes. Ultimately, the paper substantiates the efficacy of the proposed approach through empirical experimentation, demonstrating its capacity to more adeptly address the mobile office platform needs of enterprise employees.