GMAT:A Graph Modeling Method for Group Preference Prediction  

作　　者：Xiangyu Li Xunhua Guo Guoqing Chen 

机构地区：[1]International Business School,Jinan University,Zhuhai,519070,China [2]School of Economics and Management,Tsinghua University,Beijing,100084,China

出　　处：《Journal of Systems Science and Systems Engineering》2024年第4期475-493,共19页系统科学与系统工程学报（英文版）

基　　金：supported by National Natural Science Foundation of China(72293561);Research Center for Interactive Technology Industry of Tsinghua University(RCITI2022T002).

摘　　要：Preference prediction is the building block of personalized services,and its implementation at the group level helps enterprises identify their target customers effectively.Existing methods for preference prediction mainly focus on behavioral interactions to extract the associations between groups and products,ignoring the importance of other auxiliary records(e.g.,online reviews and social tags)in association detection.This paper proposes a novel method named GMAT for group preference prediction,aiming to collectively detect the sophisticated association patterns from user generated content(UGC)and behavioral interactions.In doing so,we construct a tripartite graph to collaborate these two types of data,and design a deep-learning algorithm with mutual attention module for generating the contextualized representations of groups and products.Extensive experiments on two real-world datasets show that GMAT is superior to other baselines in terms of group preference prediction.Additionally,GMAT is able to improve prediction accuracy compared with its different variants,further verifying the proposed method’s effectiveness on association pattern detection.

关 键 词：Group preference UGC tripartite graph deep learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]