Identifying Key Observers to Find Popular Information in Advance
Identifying Key Observers to Find Popular Information in Advance
复制标题
DOI：
--
复制
发表时间：
2016-07
期刊：
影响因子：
--
通讯作者：
Takuya Konishi;Tomoharu Iwata;K. Hayashi;K. Kawarabayashi
中科院分区：
文献类型：
--
作者：
Takuya Konishi;Tomoharu Iwata;K. Hayashi;K. Kawarabayashi
识别Web服务中即将流行的项目提供了重要的好处。我们试图识别能够找到潜在热门商品的用户。这种有远见的用户被称为观察者。通过将观察者添加到最喜欢的用户列表中，他们可以提前找到热门项目。为了识别有效的观察者，我们提出了一种基于特征选择的框架。这使用分类器来预测项目受欢迎程度，其中输入特征是一组用户，他们在其他项目之前采用了一项。通过对具有稀疏和非负约束的分类器进行训练，将观测器提取为参数取非零值的用户。在实验中，我们使用真实的社会书签数据集来测试我们的方法。实验结果表明，与基线方法相比，该方法能够更有效地提前发现热门商品。
Identifying soon-to-be-popular items in web services offers important benefits. We attempt to identify users who can find prospective popular items. Such visionary users are called observers. By adding observers to a favorite user list, they act to find popular items in advance. To identify efficient observers, we propose a feature selection based framework. This uses a classifier to predict item popularity, where the input features are a set of users who adopted an item before others. By training the classifier with sparse and non-negative constraints, observers are extracted as users whose parameters take a non-zero value. In experiments, we test our approach using real social bookmark datasets. The results demonstrate that our approach can find popular items in advance more effectively than baseline methods.