Improved literature recommendation system through the fusion mode of conformity bias elimination and keyword preference

Abstract

Authors are often influenced by historical experience and personal habits when selecting keywords in composing papers, and tend to choose specific keywords, resulting in a phenomenon of conformity bias. In order to improve the accuracy of literature recommendation services, it is crucial to effectively identify and eliminate the adverse effects of conformity bias. Based on the definition of a measurement for the degree of impact of conformity bias using conformity width, this paper designs an effective method for eliminating conformity bias based on one-class implicit access information. At the same time, this method further utilizes the author's own keyword preference information based on explicit weight of keywords and achieves the comprehensive fusion of various effective user access information through the design of a model. The experimental results validated the phenomenon of conformity bias in the composing process of scholars, and comprehensively analyzed and compared the characteristics and advantages of the algorithm proposed in this paper. The code is available at https://github.com/EdwardSig/CBEWC_ESWA.