社會(huì)化推薦中信任值的計(jì)算

1、社會(huì)化推薦中信任值的計(jì)算信任值計(jì)算的兩種思路 全局信任矩陣計(jì)算法 計(jì)算出社交網(wǎng)絡(luò)中每一個(gè)節(jié)點(diǎn)的固定信任值 PageRank , E-bay 局部信任矩陣計(jì)算法 在信任傳播域內(nèi)，選擇中心節(jié)點(diǎn)，計(jì)算目標(biāo)節(jié)點(diǎn)的信任值 MoleTrust關(guān)于爭(zhēng)議用戶 爭(zhēng)議用戶 Controversial Users 爭(zhēng)議用戶是指同時(shí)收到過(guò)正面評(píng)價(jià)（信任）和負(fù)面評(píng)價(jià)（不信任）的用戶，這一部分用戶在社交網(wǎng)絡(luò)中占比頗高，(more than 20% in Epinions dataset) 且信任值較難正確預(yù)測(cè)。 無(wú)爭(zhēng)議用戶 Non-Controversial Users 無(wú)爭(zhēng)議用戶是指只收到過(guò)正面評(píng)價(jià)（信任）或負(fù)面評(píng)價(jià)（

2、不信任）的用戶，這部分用戶在社交網(wǎng)絡(luò)中占絕大多數(shù)，且信任值容易被正確預(yù)測(cè)。關(guān)于爭(zhēng)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(jì)算法 Ebay計(jì)算法 類PageRank計(jì)算法 不僅考慮信任邊和不信任邊的數(shù)量，同時(shí)考慮信任邊與不信任邊的質(zhì)量PAGERANK PageRank介紹 PageRank，網(wǎng)頁(yè)排名，又稱網(wǎng)頁(yè)級(jí)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(yè)之間相互的超鏈接計(jì)算的技術(shù)，而作為網(wǎng)頁(yè)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(lái)命名。Google用它來(lái)體現(xiàn)網(wǎng)頁(yè)的相關(guān)性和重要性，在搜索引擎優(yōu)化操作中是經(jīng)常被用來(lái)評(píng)估網(wǎng)頁(yè)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(xué)發(fā)明了這項(xiàng)技術(shù)。 PageRank通過(guò)網(wǎng)絡(luò)浩瀚的超鏈接關(guān)系來(lái)確定一個(gè)頁(yè)面的等級(jí)。Google把從A頁(yè)面到B頁(yè)面的鏈接解釋為A頁(yè)面給B頁(yè)面投票，Google根據(jù)投票來(lái)源（甚至來(lái)源的來(lái)源，即鏈接到A頁(yè)面的頁(yè)面）和投票目標(biāo)的等級(jí)來(lái)決定新的等級(jí)。簡(jiǎn)單的說(shuō)，一個(gè)高等級(jí)的頁(yè)面可以使其他低等級(jí)頁(yè)面的等級(jí)提升。轉(zhuǎn)換矩陣 互聯(lián)網(wǎng)中的網(wǎng)頁(yè)可以看出是一個(gè)有向圖，其中網(wǎng)頁(yè)是結(jié)點(diǎn)，如果網(wǎng)頁(yè)A有鏈接到網(wǎng)頁(yè)B，則

5、存在一條有向邊A-B，下面是一個(gè)簡(jiǎn)單的示例：迭代計(jì)算 初試時(shí)，假設(shè)上網(wǎng)者在每一個(gè)網(wǎng)頁(yè)的概率都是相等的，即1/n，于是初試的概率分布就是一個(gè)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(gè)nX1的矩陣。下面是V1的計(jì)算過(guò)程：迭代計(jì)算 得到了V1后，再用V1去右乘M得到V2，一直下去，最終V會(huì)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(diǎn)分析 優(yōu)點(diǎn) 一次性計(jì)算 覆蓋率高 缺點(diǎn) 爭(zhēng)議用戶信任值預(yù)測(cè)精度低局部信任矩陣計(jì)算法 提高對(duì)于爭(zhēng)議用戶的信任值預(yù)測(cè)精度，選

6、擇網(wǎng)絡(luò)的局部進(jìn)行計(jì)算。 TWO STEPS： 1.去除回路 2.計(jì)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(shí)驗(yàn)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(píng)估機(jī)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