Simulation of Intelligent Fusion Clustering Recommendation for Library Collections Based on User Characteristics
In order to improve the retrieval efficiency of library collections,a user feature-based intelligent fusion clustering recommen-dation method for library collections is proposed.Firstly,the TF-IDF method is used to extract user preference features for book re-sources,and the time coefficient is incorporated into the decay function to analyze the changes in user preference features at different time periods.Based on this,the user preference features are updated.Using the K-means algorithm to cluster users based on their preference characteristics,and using the improved artificial bee colony algorithm to optimize the clustering centers and complete user clustering;Finally,based on the similarity of preference features between users,the nearest neighbor of the target user is obtained,and the similarity in ratings of book resources between the target user and the nearest neighbor is calculated.Based on the calculation results,a weighted fusion model is established to predict the user's rating of unread book resources.High rated book resources are se-lected to generate a recommendation list,achieving intelligent fusion clustering recommendation of library collections.The simulation experiment results show that the proposed method has high clustering accuracy,high coverage,good recommendation effect,and good diversity.
NETL
NSTL
万方数据
