社會化推薦中信任值的計算

1、社會化推薦中信任值的計算信任值計算的兩種思路 全局信任矩陣計算法 計算出社交網(wǎng)絡中每一個節(jié)點的固定信任值 PageRank , E-bay 局部信任矩陣計算法 在信任傳播域內(nèi)，選擇中心節(jié)點，計算目標節(jié)點的信任值 MoleTrust關于爭議用戶 爭議用戶 Controversial Users 爭議用戶是指同時收到過正面評價（信任）和負面評價（不信任）的用戶，這一部分用戶在社交網(wǎng)絡中占比頗高，(more than 20% in Epinions dataset) 且信任值較難正確預測。 無爭議用戶 Non-Controversial Users 無爭議用戶是指只收到過正面評價（信任）或負面評價（

2、不信任）的用戶，這部分用戶在社交網(wǎng)絡中占絕大多數(shù)，且信任值容易被正確預測。關于爭議用戶 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.全局信任矩陣計算法 Ebay計算法 類PageRank計算法 不僅考慮信任邊和不信任邊的數(shù)量，同時考慮信任邊與不信任邊的質(zhì)量PAGERANK PageRank介紹 PageRank，網(wǎng)頁排名，又稱網(wǎng)頁級別、Google左側(cè)排名或佩奇排名，是一種由搜索引擎根據(jù)網(wǎng)頁之間相互的超鏈接計算的技術，而作為網(wǎng)頁排名的要素之一，以Google公司創(chuàng)辦

4、人拉里佩奇（Larry Page）之姓來命名。Google用它來體現(xiàn)網(wǎng)頁的相關性和重要性，在搜索引擎優(yōu)化操作中是經(jīng)常被用來評估網(wǎng)頁優(yōu)化的成效因素之一。Google的創(chuàng)始人拉里佩奇和謝爾蓋布林于1998年在斯坦福大學發(fā)明了這項技術。 PageRank通過網(wǎng)絡浩瀚的超鏈接關系來確定一個頁面的等級。Google把從A頁面到B頁面的鏈接解釋為A頁面給B頁面投票，Google根據(jù)投票來源（甚至來源的來源，即鏈接到A頁面的頁面）和投票目標的等級來決定新的等級。簡單的說，一個高等級的頁面可以使其他低等級頁面的等級提升。轉(zhuǎn)換矩陣 互聯(lián)網(wǎng)中的網(wǎng)頁可以看出是一個有向圖，其中網(wǎng)頁是結(jié)點，如果網(wǎng)頁A有鏈接到網(wǎng)頁B，則

5、存在一條有向邊A-B，下面是一個簡單的示例：迭代計算 初試時，假設上網(wǎng)者在每一個網(wǎng)頁的概率都是相等的，即1/n，于是初試的概率分布就是一個所有值都為1/n的n維列向量V0，用V0去右乘轉(zhuǎn)移矩陣M，就得到了第一步之后上網(wǎng)者的概率分布向量MV0,（nXn）*(nX1)依然得到一個nX1的矩陣。下面是V1的計算過程：迭代計算 得到了V1后，再用V1去右乘M得到V2，一直下去，最終V會收斂，即Vn=MV(n-1)，上面的圖示例，不斷的迭代，最終V=3/9,2/9,2/9,2/9優(yōu)缺點分析 優(yōu)點 一次性計算 覆蓋率高 缺點 爭議用戶信任值預測精度低局部信任矩陣計算法 提高對于爭議用戶的信任值預測精度，選

6、擇網(wǎng)絡的局部進行計算。 TWO STEPS： 1.去除回路 2.計算步驟詳解 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.實驗數(shù)據(jù) 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評估機制 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