图 书 馆
ADMM并行新闻报道流量预测Parallel News-Article Traffic Forecasting with ADMM |
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| 课程网址: | https://videolectures.net/videos/kdd2016_ioannidis_traffic_foreca... |
| 主讲教师: | Stratis Ioannidis |
| 开课单位: | KDD 2016研讨会 |
| 开课时间: | 2025-02-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Predicting the traffic of an article, as measured by page views, is of great importance to content providers. Articles with increased traffic can improve advertising revenue and expand a provider’s user base. We propose a broadly applicable methodology incorporating meta-data and joint forecasting across articles, that involves solving a large optimization problem through the Alternating Directions Method of Multipliers (ADMM). We implement our solution using Spark, and evaluate it over a large corpus of articles and forecasting models. Our results demonstrate that our featurebased forecasting is both scalable as well as highly accurate, significantly improving forecasting predictions compared to traditional forecasting models. |
| 关 键 词: | 新闻报道; 流量预测; 预测模型 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-04-06：liyq |
| 最后编审: | 2025-04-06：liyq |
| 阅读次数: | 8 |