社会化推荐中信任值的计算

1、社会化推荐中信任值的计算信任值计算的两种思路 全局信任矩阵计算法 计算出社交网络中每一个节点的固定信任值 PageRank , E-bay 局部信任矩阵计算法 在信任传播域内，选择中心节点，计算目标节点的信任值 MoleTrust关于争议用户 争议用户 Controversial Users 争议用户是指同时收到过正面评价（信任）和负面评价（不信任）的用户，这一部分用户在社交网络中占比颇高，(more than 20% in Epinions dataset) 且信任值较难正确预测。 无争议用户 Non-Controversial Users 无争议用户是指只收到过正面评价（信任）或负面评价（

2、不信任）的用户，这部分用户在社交网络中占绝大多数，且信任值容易被正确预测。关于争议用户 controversiality level = min(#trust;#distrust) For example, a user who received 21 distrust statements and 14 trust statements has a controversiality level of 14. A user with 1 (-1) as controversiality percentage is trusted (distrusted) by all her judgers.

3、 A user whose controversiality percentage is 0 is highly controversial since other users split into 2 opinions groups of same size.全局信任矩阵计算法 Ebay计算法 类PageRank计算法 不仅考虑信任边和不信任边的数量，同时考虑信任边与不信任边的质量PAGERANK PageRank介绍 PageRank，网页排名，又称网页级别、Google左侧排名或佩奇排名，是一种由搜索引擎根据网页之间相互的超链接计算的技术，而作为网页排名的要素之一，以Google公司创办

4、人拉里佩奇（Larry Page）之姓来命名。Google用它来体现网页的相关性和重要性，在搜索引擎优化操作中是经常被用来评估网页优化的成效因素之一。Google的创始人拉里佩奇和谢尔盖布林于1998年在斯坦福大学发明了这项技术。 PageRank通过网络浩瀚的超链接关系来确定一个页面的等级。Google把从A页面到B页面的链接解释为A页面给B页面投票，Google根据投票来源（甚至来源的来源，即链接到A页面的页面）和投票目标的等级来决定新的等级。简单的说，一个高等级的页面可以使其他低等级页面的等级提升。转换矩阵 互联网中的网页可以看出是一个有向图，其中网页是结点，如果网页A有链接到网页B，则

5、存在一条有向边A-B，下面是一个简单的示例：迭代计算 初试时，假设上网者在每一个网页的概率都是相等的，即1/n，于是初试的概率分布就是一个所有值都为1/n的n维列向量V0，用V0去右乘转移矩阵M，就得到了第一步之后上网者的概率分布向量MV0,（nXn）*(nX1)依然得到一个nX1的矩阵。下面是V1的计算过程：迭代计算 得到了V1后，再用V1去右乘M得到V2，一直下去，最终V会收敛，即Vn=MV(n-1)，上面的图示例，不断的迭代，最终V=3/9,2/9,2/9,2/9优缺点分析 优点 一次性计算 覆盖率高 缺点 争议用户信任值预测精度低局部信任矩阵计算法 提高对于争议用户的信任值预测精度，选

6、择网络的局部进行计算。 TWO STEPS： 1.去除回路 2.计算步骤详解 The first step modifies the social network by ordering users based on distance from source user and keeping only trust edges that goes from users at distance n to users at distance n + 1. The second step is a simple graph walk over the modified social network,

7、starting from source user. The trust score of one user at distance x only depends on trust scores of users at distance x - 1, that are already computed and definitive.实验数据 The E dataset we used contained 132000 users, who issued 841000 statements (717000 trusts and 124000 distrusts). 85000 users rec

8、eived at least one statement. most of the users are non controversial, in the sense that all the users judging them share the same opinion. Out of the 84601 users who received at least one statement, 67511 are 0-controversial, 17090 (more than 20%) are at least 1-controversial, i.e. at least one use

9、r disagrees with the others, 1247 are at least 10-controversial, 144 are at least 40-controversial and one user is 212-controversial评估机制 The evaluation technique is a standard one in machine learning: leave-one-out. Taken one trust statement from user A to user B, we remove it from the trust network

10、 and try then to predict it using the local trust metric. We then compare the predicted trust score against the original trust statement. For the global trust metric, we compare the predicted global trust score of B against the statement issued by A on B. Two measures are derived from this evaluation technique : accuracy and coverage. Accuracy represents the error produced when predicting a score. We use Mean Absolute Error that consists in computing the absolute value of the difference between the real score and the predicted