Enhancing Next-Item Recommendation Through Adaptive User Group Modeling  

作　　者：Nengjun Zhu Lingdan Sun Jian Cao Xinjiang Lu Runtong Li 

机构地区：[1]the School of Computer Engineering and Science,Shanghai University,Shanghai 200444,China [2]the Department of Computer Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China [3]the Business Intelligence Lab,Baidu Research,Beijing 100085,China [4]the Department of Electronic Engineering,Tsinghua University,Beijing 100084,China

出　　处：《Journal of Social Computing》2023年第2期112-124,共13页社会计算（英文）

基　　金：supported by the National Natural Science Foundation of China(No.62202282);Shanghai Youth Science and Technology Talents Sailing Program(No.22YF1413700).

摘　　要：Session-based recommender systems are increasingly applied to next-item recommendations.However,existing approaches encode the session information of each user independently and do not consider the interrelationship between users.This work is based on the intuition that dynamic groups of like-minded users exist over time.By considering the impact of latent user groups,we can learn a user’s preference in a better way.To this end,we propose a recommendation model based on learning user embeddings by modeling long and short-term dynamic latent user groups.Specifically,we utilize two network units to learn users’long and short-term sessions,respectively.Meanwhile,we employ two additional units to determine the affiliation of users with specific latent groups,followed by an aggregation of these latent group representations.Finally,user preference representations are shaped comprehensively by considering all these four aspects,based on an attention mechanism.Moreover,to avoid setting the number of groups manually,we further incorporate an adaptive learning unit to assess the necessity for creating a new group and learn the representation of emerging groups automatically.Extensive experiments prove our model outperforms multiple state-of-the-art methods in terms of Recall,mean average precision(mAP),and area under curve(AUC)metrics.

关 键 词：session-based recommender user group modeling attention mechanism adaptive learning 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]