融合FP-Growth和RBM的图书推荐算法研究

Research on book recommendation algorithm integrating FP-Growth and RBM

为了解决传统图书推荐算法推荐准确性不高、效率低下的问题,本文提出了融合关联规则FP-Growth和受限玻尔兹曼机RBM的混合推荐算法用于图书推荐.FP-Growth算法能够处理复杂的非结构化数据,RBM协同过滤算法可以有效解决数据稀疏问题,通过结合两种算法组成混合推荐模型,能够弥补单一算法的不足之处,体现混合推荐模型的优势.实验结果表明,相比FP-Growth和RBM协同过滤算法,本文提出的融合算法将准确率分别提升了15.63%和7.58%,从而能够更加精准地进行图书推荐.

In order to solve the problem of low accuracy and low efficiency of traditional book recommendation algorithms,a hybrid recommendation algorithm combining association rule FP-Growth and restricted Boltzmann machine RBM is proposed for book recommendation.The FP-Growth algorithm can handle complex unstructured data,and the RBM collaborative filtering algorithm can effectively solve the problem of data sparseness.By combining two algorithms to form a hybrid recommendation model,it can make up for the shortcomings of a single algorithm and reflect the advantages of the hybrid recommendation model.The experimental results show that compared with the FP-Growth and RBM collaborative filtering algorithms,the proposed fusion algorithm increases the accuracy by 15.63%and 7.58%respectively,so that book recommendations can be performed more accurately.