图 书 馆
Twitter中用户评测的动态嵌入Dynamic Embeddings for User Profiling in Twitter |
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| 课程网址: | http://videolectures.net/kdd2018_zhang_user_profiling/ |
| 主讲教师: | Xiangliang Zhang |
| 开课单位: | 阿卜杜拉国王科技大学（KAUST） |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper, we study the problem of dynamic user profiling in Twitter. We address the problem by proposing a dynamic user and word embedding model (DUWE), a scalable black-box variational inference algorithm, and a streaming keyword diversification model (SKDM). DUWE dynamically tracks the semantic representations of users and words over time and models their embeddings in the same space so that their similarities can be effectively measured. Our inference algorithm works with a convex objective function that ensures the robustness of the learnt embeddings. SKDM aims at retrieving top-K relevant and diversified keywords to profile users’ dynamic interests. Experiments on a Twitter dataset demonstrate that our proposed embedding algorithms outperform state-of-the-art non-dynamic and dynamic embedding and topic models. |
| 关 键 词: | 动态用户配置问题; 动态用户和词嵌入模型; 流式关键字多样化模型; 动态嵌入和主题模型 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-01-28：cyh |
| 最后编审: | 2023-01-28：cyh |
| 阅读次数: | 51 |