Collaborative filtering recommendation algorithm based on user behavior and drug semantics

Abstract

When the traditional collaborative filtering algori- thm is applied to drug recommendation, the recommendation effect is not good due to the sparsity of data. In view of the above problems, this paper proposes a collaborative filtering recommendation algorithm based on user behavior and drug semantics (UBDS-CF). Firstly, we construct the purchasing behavior matrix of users and drugs, and use the weighted cosine similarity to calculate the basic similarity between drugs; then construct the category label matrix of drugs, calculate the category similarity, and extract the feature vector of drug function text using the word vector model; category and main function together constitute the drug semantic information; finally, the above two similarities are integrated in a linear weighted manner to obtain the final similarity of drugs. Experimental results show that the average absolute error and root mean square error of the algorithm are significantly reduced, which proves the effectiveness of the proposed algorithm.

Evaluation of Curriculum Implementation Effectiveness of Higher Vocational Education Based on Collaborative Filtering Algorithm

“…From the culture perspective, curriculum implementation evaluation is regarded as cultural regeneration, and curriculum implementation evaluation is the process of promoting teachers to think over teaching arrangements. Analyzing the impact of curriculum implementation assessment from diverse viewpoints holds significance in enhancing the efficacy of curriculum implementation evaluation [19]. An overview of the implementation evaluation of educational courses is shown in Figure 1.2.…”

UI Design and Optimization Method for Museum Display Based on User Behavior Recommendation

“…Obtain optimal values for feature attributes and interest cluster analysis [1]. Personalized recommendation system is a course recommendation algorithm that integrates user characteristics and interest clustering [2][3][4]. Due to the limitation of time and product display space, the different characteristics of users are counted, the characteristic attributes are weighted, and the attenuation factor is introduced at the same time, and then, the course prediction score is calculated.…”