Switching Hybrid Method Based on User Similarity and Global Statistics for Collaborative Filtering

Abstract

Collaborative filtering (CF) is a technique used in recommender systems to provide meaningful suggestions based on known feedback obtained from like-minded users. The measure of similarity plays a critical role in the performance of neighborhood-based CF methods. However, conventional similarity measures suffer from limitations because they only consider the direction of the rating vectors. We propose a novel similarity measure that considers the semantic nuances of the ratings; in particular, it weights the contributions of ratings in proportion to the users' degree of indifference towards the items. Additionally, to address the sparsity problem that affects the performance of CF techniques, we propose a switching hybrid method that predicts user ratings based on either our custom similarity measure or through user and item biases. We evaluated the proposed method on six different datasets and compared it with other CF methods. The results show that the proposed recommender consistently outperforms those using conventional similarity measures when the sparsity of the dataset is high.

Method of Ideological and Political Teaching Resources in Universities Based on School-Enterprise Cooperation Mode

“…School-enterprise cooperation teaching resource platform is designed to overcome the boring of traditional learning mode, enhance the fun of learning process, meet the personalized learning needs of students, and facilitate teacher-student interaction [6]. e teaching resource platform is divided into five functional modules as follows: user information management, teaching resource management, resource statistical analysis, course management, and micro-lesson management, among which teaching resource management and course management are the core parts of the platform management [7].…”

“…In formula (7), W represents the weight of behavior and ϕ represents the total number of learning behaviors. Combined with the calculation results of the above information entropy weight value, users' preferences for different ideological and political teaching resources are calculated respectively.…”