基于评分填充与信任信息的混合推荐算法

针对推荐系统的数据稀疏性导致的推荐效果不佳的问题，提出一种基于评分填充与信任信息的混合推荐的算法RTWSO（Real-value user item restricted Boltzmann machine Trust WSO）。首先，使用改进的受限玻尔兹曼机模型对评分矩阵进行填充，以缓解评分矩阵的稀疏性问题；其次，从信任关系中提取信任与被信任关系，并通过基于矩阵分解的隐含信任关系相似度来解决信任信息稀疏的问题，而且对原有算法进行了包含信任信息的修正，以提高推荐准确度；最后，通过加权Slope One（WSO）算法对矩阵填充与信任相似度信息加以整合，并对评分数据进行预测。在Epinions与Ciao数据集中验证算法性能，可见所提出混合推荐算法较组成算法在推荐准确度上提升3%以上，较现有社会化推荐算法SocialIT（Social recommendation algorithm based on Implict similarity in Trust）在推荐准确度上提升1.2%以上。实验结果表明，所提出的基于评分填充与信任信息的混合推荐算法在一定程度上提高了推荐准确度。

Hybrid recommendation algorithm based on rating filling and trust information

Aiming at the problem of poor recommendation effect caused by the data sparsity of the recommendation system, a hybrid recommendation algorithm based on rating filling and trust information was proposed namely RTWSO (Real-value user item restricted Boltzmann machine Trust Weighted Slope One). Firstly, the improved restricted Boltzmann machine model was used to fill the rating matrix, so as to alleviate the sparseness problem of the rating matrix. Secondly, the trust and trusted relationships were extracted from the trust relationship, and the matrix decomposition based implicit trust relationship similarity was also used to solve the problem of trust relationship sparsity. The modification including trust information was performed to the original algorithm, improving the recommendation accuracy. Finally, the Weighted Slope One (WSO) algorithm was used to integrate the matrix filling and trust similarity information as well as predict the rating data. The performance of the proposed hybrid recommendation algorithm was verified on Epinions and Ciao datasets. It can be seen that the proposed hybrid recommendation algorithm has the recommendation accuracy improved by more than 3% compared with the composition algorithm, and recommendation accuracy increased by more than 1.2% compared with the existing social recommendation algorithm SocialIT (Social recommendation algorithm based on Implict similarity in Trust). Experimental results show that the proposed hybrid recommendation method based on rating filling and trust information, improves the recommended accuracy to a certain extent.