图 书 馆
深入感知用户：从多个电子商务任务中学习通用用户表示Perceive Your Users in Depth: Learning Universal User Representations from Multiple E‑commerce Tasks |
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| 课程网址: | http://videolectures.net/kdd2018_ni_perceive_depth/ |
| 主讲教师: | Yabo Ni |
| 开课单位: | 阿里巴巴集团 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Tasks such as search and recommendation have become increasingly important for E-commerce to deal with the information overload problem. To meet the diverse needs of different users, personalization plays an important role. In many large portals such as Taobao and Amazon, there are a bunch of different types of search and recommendation tasks operating simultaneously for personalization. However, most of current techniques address each task separately. This is suboptimal as no information about users shared across different tasks. In this work, we propose to learn universal user representations across multiple tasks for more effective personalization. In particular, user behavior sequences (e.g., click, bookmark or purchase of products) are modeled by LSTM and attention mechanism by integrating all the corresponding content, behavior and temporal information. User representations are shared and learned in an end-to-end setting across multiple tasks. Benefiting from better information utilization of multiple tasks, the user representations are more effective to reflect their interests and are more general to be transferred to new tasks. We refer this work as Deep User Perception Network (DUPN) and conduct an extensive set of offline and online experiments. Across all tested five different tasks, our DUPN consistently achieves better results by giving more effective user representations. Moreover, we deploy DUPN in large scale operational tasks in Taobao. Detailed implementations, e.g., incremental model updating, are also provided to address the practical issues for the real world applications. |
| 关 键 词: | 信息过载; 增量模型; 实际问题 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-07：chenjy |
| 最后编审: | 2023-03-07：chenjy |
| 阅读次数: | 38 |