Abstract: To solve the sparse and cold start problems of recommendation system, a Trust Regularization Euclidean Embedding (TREE) algorithm by fusing trust information was proposed. Firstly, the Euclidean embedding model was employed to embed the user and project in the unified low-dimensional space. Secondly, to measure the trust information, both the project participation degree and user common scoring factor were brought into the user similarity calculation formula. Finally, a regularization term of social trust relationship was added to the Euclidean embedding model, and trust users with different preferences were used to constrain the location vectors of users and generate the recommendation results. In the experiments, the proposed TREE algorithm was compared with the Probabilistic Matrix Factorization (PMF), Social Regularization (SoReg), Social Matrix Factorization (SocialMF) and Recommend with Social Trust Ensemble (RSTE) algorithms. When dimensions are 5 and 10, TREE algorithm has the Root Mean Squared Error (RMSE) decreased by 1.60% and 5.03% respectively compared with the optimal algorithm RSTE on the dataset Filmtrust.While on the dataset Epinions, the RMSE of TREE algorithm was respectively 1.12% and 1.29% lower than that of the optimal algorithm SocialMF. Experimental results show that TREE algorithm further alleviate the sparse and cold start problems and improves the accuracy of scoring prediction.

Key words: social recommendation, Euclidean embedding, collaborative filtering, matrix factorization, trust information

摘要： 为了改善推荐系统存在的稀疏性和冷启动问题，提出一种融合信任信息的欧氏嵌入推荐（TREE）算法。首先，利用欧氏嵌入模型将用户和项目嵌入到统一的低维空间中；其次，在用户相似度计算公式中引入项目参与度和用户共同评分因子以度量信任信息；最后，在欧氏嵌入模型中加入社交信任关系正则化项，利用不同偏好的信任用户约束用户的位置向量并生成推荐结果。实验将TREE算法与概率矩阵分解（PMF）、社会正则化（SoReg）模型、社交的矩阵分解（SocialMF）模型、社交信任集成模型（RSTE）四种算法进行对比，当维度为5和10时，在Filmtrust数据集上TREE算法的均方根误差（RMSE）比最优的RSTE算法分别降低了1.60%、5.03%，在Epinions数据集上TREE算法的RMSE比最优的社交矩阵分解模型（SocialMF）算法分别降低了1.12%、1.29%。实验结果表明，TREE算法能进一步缓解稀疏和冷启动问题，提高评分预测的准确性。

关键词: 社会化推荐, 欧氏嵌入, 协同过滤, 矩阵分解, 信任信息