融合信任隐含相似度与评分相似度的社会化推荐  

Social recommendation combining trust implicit similarity and score similarity

作　　者：周寅莹 章梦怡 余敦辉 朱明[1] ZHOU Yinying;ZHANG Mengyi;YU Dunhui;ZHU Ming(School of Computer and Information Engineering,Hubei University,Wuhan Hubei 430062,China;Hubei Provincial Engineering and Technology Research Center for Education Informationization(Hubei University),Wuhan Hubei 430062,China)

机构地区：[1]湖北大学计算机与信息工程学院,武汉430062 [2]湖北省教育信息化工程技术研究中心(湖北大学),武汉430062

出　　处：《计算机应用》2022年第12期3671-3678,共8页journal of Computer Applications

基　　金：国家重点研发计划项目(2018YFB1003801);国家自然科学基金资助项目(61977021);湖北省技术创新专项(重大项目)(2018ACA13)。

摘　　要：针对现有的社会化推荐算法大都忽略了物品间的关联关系对推荐精度的影响,并且未能将用户评分与信任数据进行有效结合的问题,提出一种融合信任隐含相似度与评分相似度的社会化推荐算法(SocialTS)。首先,将用户间的评分相似度与信任隐含相似度进行线性组合以得到用户间可靠的相似朋友;然后,将信任关系融入到项目的相关性分析中,从而得到修正后的相似项目;最后,将相似用户、项目作为正则项添加到矩阵分解(MF)模型下,从而获取用户、项目更准确的特征表示。实验结果表明,当潜在特征维度为10时,与主流的社会化推荐算法TrustSVD相比,SocialTS在FilmTrust和CiaoDVD数据集上的均方根误差(RMSE)分别降低了4.23%和8.38%,平均绝对误差(MAE)分别降低了4.66%和6.88%。SocialTS不仅可以有效改善用户冷启动问题,还能较为准确地预测不同评分数量下用户的实际评分,且具有良好的鲁棒性。Focused on the issue that the most existing social recommendation algorithms ignore the influence of the association relationship between items on the recommendation accuracy, and fail to effectively combine user ratings with trust data, a Social recommendation algorithm combing Trust implicit similarity and Score similarity(SocialTS) was proposed.Firstly, the score similarity and trust implicit similarity between users were combined linearly to obtain reliable similar friends among users. Then, the trust relationship was integrated into the correlation analysis of items, and the modified similar items were obtained. Finally, similar users and items were added to the Matrix Factorization(MF) model as regularization terms, thereby obtaining more accurate feature representations of users and items. Experimental results show that on FilmTrust and CiaoDVD datasets, when the latent feature dimension is 10, compared with the mainstream social recommendation algorithm Trust-based Singular Value Decomposition(TrustSVD), SocialTS has the Root Mean Square Error(RMSE) reduced by 4. 23% and 8. 38% respectively, and the Mean Absolute Error(MAE) reduced by 4. 66% and 6. 88% respectively. SocialTS can not only effectively improve users’ cold start problem, but also accurately predict users’ actual ratings under different numbers of ratings, and has good robustness.

关 键 词：社会化推荐 冷启动 信任隐含相似度 信任关系 矩阵分解 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]