Information Service Innovation in College Libraries Based on Big Data Analysis

This paper first proposes a real-time recommendation technique based on user images and utilizes multi-dimensional small data fusion for user image construction, which in turn gives a personalized wisdom service recommendation structure. Secondly, user long and short-term preference fusion is carried out by introducing a self-attention mechanism and GRU network, and then personalized retrieval recommendation based on user long and short-term preference is realized. Finally, an investigation is carried out to determine the effectiveness of the personalized wisdom information service model through empirical analysis. The results show that the mean value of literature and history in the user portrait category III is 1.553, the change in the borrowing of books in the I major category decreased by 31.94% from 2018 to 2022, and the average satisfaction of users for the personalized recommendation service is 83.52%. This shows that the personalized intelligent information service model of the college library can realize the innovation of library information service and further improve the satisfaction of users for the college libraries.