Score Prediction Algorithm Combining Deep Learning and Matrix Factorization in Sensor Cloud Systems
Score Prediction Algorithm Combining Deep Learning and Matrix Factorization in Sensor Cloud Systems
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DOI：
10.1109/access.2020.3035162
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发表时间：
2021-01-01
期刊：
影响因子：
3.9
通讯作者：
Wang, Ying
中科院分区：
文献类型：
--
作者：
Gong, Jibing;Du, Weixia;Wang, Ying
在这个数据规模呈指数级增长的时代，信息过载已成为一个迫切的问题，而使用日益灵活的传感器云系统（SCS）进行数据收集已成为主流趋势。推荐算法可以搜索海量数据集，根据用户的兴趣发现满足其需求的信息。为了提高推荐评分的准确性，本文提出了一种结合深度学习和矩阵分解的评分预测算法。为了解决评分数据稀疏的问题，我们的研究采用传感器云系统来收集数据信息，对收集到的信息进行预处理，然后使用深度学习模型结合显式和隐式反馈来生成推荐。所提出的算法，MF-NeuRec，融合矩阵分解和NeuRec模型得分预测算法相结合。该算法采用基于用户和基于项目的NeuRec算法来提取隐式反馈数据下的用户和项目的特征向量。在显示反馈数据下，通过矩阵分解将得到的用户和项目特征向量按一定比例进行融合。通过该算法得到的用户和项目的特征向量合并和分析，以预测用户将如何评价项目。实验表明，该算法能够提高推荐的准确率。
In this era of exponential growth in the scale of data, information overload has become an urgent problem, and the use of increasingly flexible sensor cloud systems (SCS) for data collection has become a mainstream trend. Recommendation algorithms can search massive data sets to uncover information that meets the needs of users based on their interests. To improve the accuracy of recommendation scoring, this article proposes a score prediction algorithm that combines deep learning and matrix factorization. To address the problem of sparse scoring data, our study employs a sensor cloud system to collect data information, preprocesses the collected information, and then uses a deep learning model combined with explicit and implicit feedback to generate recommendations. The proposed algorithm, MF-NeuRec, combines fusion matrix decomposition and the NeuRec model score prediction algorithm. The algorithm employs user-based and item-based NeuRec algorithms to extract the feature vectors of users and items under implicit feedback data. The obtained user and item feature vectors are integrated in a certain ratio through the use of matrix decomposition under the display feedback data. The user and item feature vectors obtained by the algorithm are merged and analyzed to predict how users will rate items. Experiments demonstrate that the algorithm can improve the accuracy of recommendations.