计算机工程 ›› 2019, Vol. 45 ›› Issue (6): 237-241,248.doi: 10.19678/j.issn.1000-3428.0051131

所属专题: 机器学习

• 人工智能及识别技术 • 上一篇    下一篇

基于终身机器学习的主题挖掘与评分预测联合模型

刘一宁,申彦明   

  1. 大连理工大学 电子信息与电气工程学部,辽宁 大连 116024
  • 收稿日期:2018-04-09 出版日期:2019-06-15 发布日期:2019-06-15
  • 作者简介:刘一宁(1984—),男,博士研究生,主研方向为推荐算法、自然语言处理;申彦明,教授、博士。
  • 基金项目:

    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)。

Topic mining and ratings prediction joint model based on lifelong machine learning

LIU Yining,SHEN Yanming   

  1. Faculty of Electronic Information and Electrical Engineering,Dalian University of Technology,Dalian,Liaoning 116024,China
  • Received:2018-04-09 Online:2019-06-15 Published:2019-06-15

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。

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

Abstract:

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.

Key words: text topic model, recommendation algorithm, Lifelong Machine Learning(LML), ratings prediction, collaborative filtering

中图分类号: