计算机应用
計算機應用
계산궤응용
COMPUTER APPLICATION
2014年
z2期
151-153,158
,共4页
社交图谱%欺诈检测%监督学习%自然语言处理%语义识别
社交圖譜%欺詐檢測%鑑督學習%自然語言處理%語義識彆
사교도보%기사검측%감독학습%자연어언처리%어의식별
social graph%fraud detection%supervised learning%Natural Language Processing ( NLP )%semantic recognition
为了改善电商虚假评论自动识别的效果，首先从传统的监督学习方法入手对网上商品评论的真实性进行判断，进而提出了利用社交图谱识别虚假评论。这种方法基于一种假设，就是同类用户通常在是否欺骗等行为上有相似性，将其结合传统的分类学习框架进行训练分类，实验结果显示社交图谱的方法能提高5'的识别准确率。
為瞭改善電商虛假評論自動識彆的效果，首先從傳統的鑑督學習方法入手對網上商品評論的真實性進行判斷，進而提齣瞭利用社交圖譜識彆虛假評論。這種方法基于一種假設，就是同類用戶通常在是否欺騙等行為上有相似性，將其結閤傳統的分類學習框架進行訓練分類，實驗結果顯示社交圖譜的方法能提高5'的識彆準確率。
위료개선전상허가평론자동식별적효과，수선종전통적감독학습방법입수대망상상품평론적진실성진행판단，진이제출료이용사교도보식별허가평론。저충방법기우일충가설，취시동류용호통상재시부기편등행위상유상사성，장기결합전통적분류학습광가진행훈련분류，실험결과현시사교도보적방법능제고5'적식별준학솔。
For the task of improving the performance of fake comments recognition in E-commerce, this paper firstly started with the traditional supervised learning methods to judge the truth of the online-commodities comments, then proposed the model of the social graph to identify fake comments. This method is based on an assumption that similar users usually have similar similarities on whether deception. The experimental results show that the social graph method can improve the recognition accuracy by 5' .
