Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System  

作　　者：Weiming Huang Baisong Liu Zhaoliang Wang 

机构地区：[1]Faculty of Electrical Engineering and Computer Science,Ningbo University,Ningbo,315211,China [2]Inner Mongolia Metal Material Research Institute,Baotou,014000,China

出　　处：《Computers, Materials & Continua》2024年第6期4449-4469,共21页计算机、材料和连续体（英文）

基　　金：supported by the National Natural Science Foundation of China(No.62271274).

摘　　要：In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.

关 键 词：Collaborative filtering citation networks variational inference poisson factorization tag recommendation 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]