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Received:July 13, 2018 Revised:August 09, 2018
Received:July 13, 2018 Revised:August 09, 2018
中文摘要: 针对当用户评分较少时，推荐系统由于数据稀疏推荐性能显著降低这一问题，介绍了协同深度学习算法（Collaborative In Deep Learning，CIDL）.本算法首先对大量数据进行深度学习，然后对数据文本进行挖掘提取词汇表，最后对评级（反馈）矩阵进行协同过滤，从而得出对用户的推荐项目.本文使用真实的电影数据进行实验，与另外四种优秀算法进行对比，证明该算法可以真实有效得解决由于数据稀疏使得性能降低的问题，并提高推荐的准确度.
Abstract:For the problem that when the user score is not enough, the recommender system significantly reduces the data sparse recommendation performance, a Collaborative In Deep Learning algorithm (CIDL) is proposed. The algorithm firstly conducts the deep learning on a large amount of data, and then performs collaborative filtering on the rating (feedback) matrix to arrive at a recommendation item for the user. This study uses real movie data to test and to compare it with the other four excellent algorithms. It is proved that CIDL can effectively solve the problem of reduced performance due to data sparseness and improve the accuracy of the recommendation.
keywords: recommender systems deep learning collaborative filtering text mining stacked denoising autoencoders
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冯楚滢,司徒国强,倪玮隆.协同深度学习推荐算法研究.计算机系统应用,2019,28(1):169-175
FENG Chu-Ying,SITU Guo-Qiang,NI Wei-Long.Research on Collaborative Deep Learning Recommendation Algorithm.COMPUTER SYSTEMS APPLICATIONS,2019,28(1):169-175
冯楚滢,司徒国强,倪玮隆.协同深度学习推荐算法研究.计算机系统应用,2019,28(1):169-175
FENG Chu-Ying,SITU Guo-Qiang,NI Wei-Long.Research on Collaborative Deep Learning Recommendation Algorithm.COMPUTER SYSTEMS APPLICATIONS,2019,28(1):169-175
