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清华大学学报(自然科学版)  2017, Vol. 57 Issue (10): 1014-1021    DOI: 10.16511/j.cnki.qhdxxb.2017.25.039
  计算机科学与技术 本期目录 | 过刊浏览 | 高级检索 |
基于深度学习加强的混合推荐方法
张敏, 丁弼原, 马为之, 谭云志, 刘奕群, 马少平
清华大学 计算机系, 清华信息科学与技术国家实验室(筹), 智能技术与系统国家重点实验室, 北京 100084
Hybrid recommendation approach enhanced by deep learning
ZHANG Min, DING Biyuan, MA Weizhi, TAN Yunzhi, LIU Yiqun, MA Shaoping
State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
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摘要 近年来基于矩阵分解的协同过滤算法在评分预测上取得了显著成果,但仍未能很好地解决冷启动、数据稀疏等问题。因此,如何将评论信息引入推荐系统以缓解上述问题成为研究的热点之一。该文尝试基于深度学习来加强个性化推荐,提出将层叠降噪自动编码器(stacked denoising auto-encoder,SDAE)与隐含因子模型(latent factor model,LFM)相结合的混合推荐方法,综合考虑评论文本与评分,以提升推荐模型对潜在评分预测的准确性。在常用大规模公开Amazon数据集上进行的测试结果表明:与传统推荐模型相比,该文提出的方法可有效提高评分预测的准确性,性能提升最高可达64.43%。
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张敏
丁弼原
马为之
谭云志
刘奕群
马少平
关键词 深度学习协同过滤混合推荐    
Abstract:Collaborative filtering based on matrix factorization has been very successful, while cold-start and data sparseness problems have not been well resolved. Hence, many studies have attempted to include review information into rating predictions. This paper presents a hybrid model that introduces deep learning into recommendation system with collaborative filtering. The algorithm combines a stacked denoising auto encoder (SDAE) with a latent factor model (LFM) to make use of both review and rating information to improve the rating predictions. Evaluations on a large, commonly used Amazon dataset show that this approach significantly improves the rating prediction accuracy in comparison with traditional models, with up to 64.43% better predictions.
Key wordsdeep learning    collaborative filtering    hybrid recommendation
收稿日期: 2016-12-13      出版日期: 2017-10-15
ZTFLH:  TP391.2  
引用本文:   
张敏, 丁弼原, 马为之, 谭云志, 刘奕群, 马少平. 基于深度学习加强的混合推荐方法[J]. 清华大学学报(自然科学版), 2017, 57(10): 1014-1021.
ZHANG Min, DING Biyuan, MA Weizhi, TAN Yunzhi, LIU Yiqun, MA Shaoping. Hybrid recommendation approach enhanced by deep learning. Journal of Tsinghua University(Science and Technology), 2017, 57(10): 1014-1021.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2017.25.039  或          http://jst.tsinghuajournals.com/CN/Y2017/V57/I10/1014
  图1 SELFM 模型框架
  表1 Amazon数据集
  表2 SELFM 与SVD++/LFM/LDAGLFM/BoWLF的性能(MSE)对比
  表3 SDAE层数对模型性能(MSE)的影响
  表4 正则项系数对模型性能(MSE)的影响
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