基于终身机器学习的主题挖掘与评分预测联合模型  

Topic Mining and Ratings Prediction Joint Model Based on Lifelong Machine Learning

作　　者：刘一宁 申彦明 LIU Yining;SHEN Yanming(Faculty of Electronic Information and Electrical Engineering,Dalian University of Technology,Dalian,Liaoning 116024,China)

机构地区：[1]大连理工大学电子信息与电气工程学部

出　　处：《计算机工程》2019年第6期237-241,248,共6页Computer Engineering

基　　金：国家自然科学基金重点项目(61432002);中央高校基本科研业务费专项资金(DUT17ZD303)

摘　　要：为充分利用历史知识,提高评分预测精度,基于终身机器学习(LML)机制提出一种同时挖掘用户评分和评论的推荐模型。在执行任务时积累知识并用于后续任务的训练,提高评分预测精度。在真实数据集上的实验结果表明,与无LML能力的模型相比,该模型预测评分的均方误差降低5.4‰,且随着知识的积累,误差不断降低,提高了主题词语分类的精度。In order to make full use of the historical knowledge and improve the accuracy of rating prediction,a recommendation model based on Lifelong Machine Learning(LML) is proposed to mine both user ratings and comments.The model accumulates knowledge from previous tasks and utilizes it in future tasks to help improve the rating prediction accuracy.Experimental results on real datasets show that compared with models without LML ability,the mean square error of the predicted ratings of this model is reduced by 5.4‰,and with the accumulation of knowledge,its error is continuely dropped.The accuracy of topic word classification results is improved.

关 键 词：文本主题模型 推荐算法 终身机器学习 评分预测 协同过滤 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]