Research on Library Recommendation Reading Service System Based on Adaptive Algorithm

Abstract

In order to facilitate the user recommendation service system of library access, predict the future number of users and hobbies, and provide decision-making basis for e-commerce enterprises, this paper designs an adaptive Web Recommendation System Framework Self-Adaptive Websites Recommendation System. It is divided into two parts: offline parts and online components. The former includes data collection, preprocessing and frequent access pattern mining. The latter generates recommendation sets based on existing mining rules and user’s current access behaviour of offline components, and realizes adaptive online recommendation service. Taking the university library as an example, this paper uses the sliding window method to obtain the current user access path, then use association rule algorithm based on aggregation tree to generate association rule set. After getting association rule set, we use recommendation set generation algorithm to generate recommendation set. The results show that both theoretical analysis and experiment indicate that the method is effective and feasible.