Context-aware recommendations via a tree-based ensemble framework

摘要：

　　Incorporating contextual information is very useful to improve the accuracy of personalized recommendations.However,how to uti-lize the contexts more efficiently are always confusing researchers,the existing work ignores the connections among different contexts,and even suffers from data sparsity.Cross-context behavior trans-fer has been tested to help overcome the problems.In this paper,we propose a tree-based ensemble framework of exploiting behav-ioral relations between different contexts to make context-aware recommendations.Specifically,our framework is to make recom-mendations for the target context based an ensemble of different user/item feature vectors learnt from other contexts with a regu-larization term.The empirical result and analysis on real-life data demonstrate that our framework achieves a significant increase in recommendation accuracy.

关键词： Collaborative Filtering Matrix Factorization Context-aware Rec-ommender Systems Ensemble Learning

作者: Ke Ji Yahan Yuan Kun Ma Runyuan Sun Zhenxiang Chen Jun Wu

作者单位: School of Information Science and Engineering University of Jinan Jinan 250022,China School of Computer and Information Technology Beijing Jiaotong University Beijing 100044,China

会议类型: 国际会议

会议名称: 2019国图灵大会(ACM Turing Celebration conference-China 2019 )

会议地点: 成都

会议语种:英文

页码: 3-7

在线出版日期: 2019-05-17（万方平台首次上网日期，不代表论文的发表时间）