摘要
在数据量过于庞大的情况下,RBM模型所输出的推荐结果会比较宽泛。此外,目前众多的协同过滤算法无法对巨大的数据集进行更好的处理。所以,尝试通过深度学习来对个性化推荐进行加强,指出把受限波尔兹曼机与隐含因子模型相结合的混合推荐方法。首先用RBM算法生成候选集,并对候选集的稀疏矩阵进行评分预测,然后使用LFM对候选结果进行排序,进而选择最优方案进行推荐。使用大型公开数据集对本文算法进行反复验证,通过测试可以看出,相比较于传统的推荐模型,本文所提阐述的方式能够有效提高评分预测的精准度。
In the case where the amount of data is too large, the recommended results output by the RBM model will be broader. Besides, many collaborative filtering algorithms currently do not handle large data sets better. So, we try to use the deep learning technology to strengthen the personalized recommendation model. We propose a hybrid recommendation model combining the bound Boltzmann model and the hidden factor model. First, we use the RBM algorithm to generate candidate sets, and score the sparse matrix of the candidate set. Then we use the LFM model to sort the candidate results and select the optimal solution for recommendation. The hybrid model is validated using used large public datasets. It can be seen from the verification that compared with the traditional recommendation model, the proposed method can improve the accuracy of the score prediction.
作者
王卫兵
张立超
徐倩
WANG Wei-bing;ZHANG Li-chao;XU Qian(School of Computer Science and Technology,Harbin University of Science and Technology,Harbin 150080,China;Harbin Branch of Heilongjiang Power corporation,Harbin 150080,China)
出处
《哈尔滨理工大学学报》
CAS
北大核心
2020年第5期62-67,共6页
Journal of Harbin University of Science and Technology
基金
国家自然科学基金(61305001)。
关键词
推荐算法
深度学习
RBM模型
LFM模型
recommendation algorithm
deep learning
restricted boltzmann machine
latent factor model