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%。
Collaborative filtering based on matrix faetorization 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.
出处
《清华大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2017年第10期1014-1021,共8页
Journal of Tsinghua University(Science and Technology)
基金
国家自然科学基金资助项目(61672311
61532011)
关键词
深度学习
协同过滤
混合推荐
deep learning
collaborative filtering
hybrid recommendation
