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摘要: 针对推荐系统中普遍存在的数据稀疏和冷启动等问题，本文结合用户自身评分与用户的社会信任关系构建推荐模型，提出了一种基于信任关系传递的社会网络推荐算法（Trust transition recommendation model，TTRM）.该方法首先通过计算信任网络中节点的声望值与偏见值来发现信任网络中的不可信节点，并通过对其评分权重进行弱化来减轻其对信任网络产生的负面影响.其次，算法又利用朋友的信任矩阵对用户自身的特征向量进行修正，解决了用户特征向量的精准构建及信任传递问题.同时为了实现修正误差的最小化，算法利用推荐特性进行用户相似度计算并通过带有社会正则化约束的矩阵分解技术实现社会网络推荐.实验结果表明，TTRM算法较传统的社会网络推荐算法在性能上具有显著提高.Abstract: To deal with the data sparsity and cool boot problem, a new method by means of trust relations called trust transition recommendation model (TTRM), as well as user rating and users' social trust network, is proposed. The first step of the methed is to spot the untrustworthy nodes in the trust network through their reputation and deviation values and abate their negative effects on trust network by weakening their rating weights. Secondly, the method revises the users' feature vector from their friends' trust matrix to solve the problems like users' feature vector accuracy establishment and trust transmission. Meanwhile, in order to minimize the round-off error, it calculates the similarity of users based on the recommendation features and realizes social network recommendation through matrix factorization with social regularization constraints. The results of experiments of TTRM on public dataset reveal that the new recommendation performare has been greatly improved compared to the traditional collaborative recommendation.
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Key words:
- Social network /
- recommendation /
- trust /
- matrix factorization /
- regulation
1) 本文责任编委 赵铁军 -
图 2 基于用户信任关系推荐的概率图模型
Fig. 2 Graphic model for recommendation based on trust relationship
图 5 不同维度与不同训练集比例下参数$\lambda_B$的MAE结果
Fig. 5 Effect experiment of parameter $\lambda_B$ under different training percent
图 6 参数$\alpha$的影响实验(维度$=10$)
Fig. 6 Impact experiment of parameter (dimensionality$=10$)
节点1 节点2 节点3 偏见值(Bias) 0.13 0.08 -0.14 声望值(Prestige) -0.33 0.73 -1.00 
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迭代次数 节点1 节点2 节点3 偏见值 声望值 偏见值 声望值 偏见值 声望值 0 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 1 0.10 0.00 -0.25 0.8 -0.25 0.00 2 0.12 -0.30 0.01 0.75 -0.16 -0.75 3 0.13 -0.33 0.08 0.73 -0.15 -1.00 4 0.13 -0.33 0.08 0.73 -0.14 -1.00 5 0.13 -0.33 0.08 0.73 -0.14 -1.00
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表 3 不同用户评价数量下各推荐算法对比结果
Table 3 Comparative results of different algorithms under different user evaluation number
评价数量 0 1~5 6~10 11~20 21~40 4~80 81~160 161~320 321~640 $>640$ 90% Trust 1.79 1.25 1.08 1.02 1.00 0.98 0.95 0.92 0.90 0.88 STE 1.69 1.18 1.03 1.00 0.99 0.97 0.95 0.92 0.90 0.88 SocialMF 1.32 1.12 1.01 0.98 0.93 0.91 0.90 0.88 0.86 0.85 TTRM 1.07 1.02 0.95 0.90 0.88 0.86 0.85 0.84 0.83 0.82 80% Trust 1.85 1.28 1.09 1.04 1.02 0.99 0.97 0.95 0.92 0.90 STE 1.68 1.15 1.05 1.03 1.00 0.96 0.95 0.92 0.91 0.89 SocialMF 1.23 1.10 1.02 0.98 0.97 0.94 0.93 0.91 0.90 0.88 TTRM 1.09 1.05 0.95 0.94 0.93 0.92 0.91 0.90 0.88 0.86 50% Trust 1.89 1.32 1.13 1.09 1.06 1.03 1.01 0.99 0.94 0.92 STE 1.75 1.25 1.10 1.07 1.03 0.99 0.97 0.95 0.88 0.86 SocialMF 1.35 1.15 1.08 1.02 1.00 0.98 0.95 0.93 0.85 0.85 TTRM 1.12 1.10 0.98 0.97 0.94 0.93 0.92 0.91 0.89 0.85 20% Trust 1.98 1.30 1.13 1.12 1.11 1.11 1.09 1.04 1.04 1.03 STE 1.78 1.23 1.10 1.08 1.07 1.06 1.05 1.02 1.02 1.01 SocialMF 1.42 1.21 1.09 1.05 1.01 1.02 0.98 0.94 0.89 0.89 TTRM 1.13 1.10 1.00 0.98 0.97 0.96 0.95 0.94 0.88 0.88
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表 4 各推荐算法的性能对比结果
Table 4 Performance comparison results of different recommendation algorithm
UserMean ItemMean PMF NMF Trust SocialMF TTRM 90% MAE 0.913 0.877 0.865 0.871 0.832 0.802 0.789 RMSE 1.169 1.238 1.154 1.162 1.101 1.051 1.021 URMSE 1.740 1.652 1.156 1.142 1.132 0.937 0.902 80% MAE 0.929 0.891 0.889 0.895 0.854 0.813 0.801 RMSE 1.182 1.259 1.177 1.183 1.126 1.053 1.028 URMSE 1.802 1.756 1.215 1.192 1.168 0.988 0.875 50% MAE 0.932 0.955 0.923 0.921 0.912 0.875 0.832 RMSE 1.192 1.263 1.185 1.193 1.236 1.089 1.092 URMSE 1.820 1.752 1.237 1.214 1.192 1.017 1.002 20% MAE 0.946 0.957 0.932 0.929 0.918 0.904 0.885 RMSE 1.205 1.206 1.185 1.123 1.056 1.094 1.085 URMSE 1.840 1.756 1.255 1.210 1.177 1.036 1.011
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