Application of intelligent recommendation system based on deep learning algorithm in university library talent training

Abstract

Intelligent recommendation system can greatly save users' time and energy by asking for relevant information and providing powerful guidance and help to users. Deep learning algorithm has become the most popular research direction in the past decade due to its powerful expression ability, whether it is the ability to fit data patterns or the ability to mine data feature combinations. This paper takes the university library as the main research data. The data comes from the original data of a university library for five years, including the collection of book information, the reader information of borrowing books, the book borrowing records, and the book related information with research and experimental data is selected. At the same time, in order to alleviate the start-up problem of new users, college library, as a new form of traditional library, combined with the resources of traditional library, analyzes the role of college library in college talent training in the intelligent recommendation system of college library, thinks about the driving force of college library service innovation, and discusses some measures of library service innovation under the background of talent training. Through the study of the existing book recommendation system, the shortcomings of the existing recommendation service are found, and a new service architecture of the recommendation system is proposed. The artificial intelligence recommendation algorithm is applied to achieve the purpose of intelligent book recommendation and provide the best service for teachers and students.

Analysis of gesture recognition applications based on deep learning

“…Temporal Information Processing: Artificial intelligence gesture recognition often involves processing time series data because gestures typically contain dynamic movements. RNNs are wellsuited for handling time series data in deep learning models and can capture temporal features of gestures, such as hand trajectories and gesture changes [8,9].…”