图 书 馆
基于协因子化模型的项目推荐和知识图完成的迁移学习Transfer Learning for Item Recommendations and Knowledge Graph Completion in Item Related Domains via A Co-Factorization Model |
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| 课程网址: | http://videolectures.net/eswc2018_piao_transfer_learning/ |
| 主讲教师: | Guangyuan Piao |
| 开课单位: | 爱尔兰国立大学 |
| 开课时间: | 2018-07-10 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | With the popularity of Knowledge Graphs (KGs) in recent years, there have been many studies leveraging the abundant background knowledge available in KGs for the task of item recommendations. However, little attention has been paid to the incompleteness of KGs when leveraging knowledge from them. In addition, previous studies have mainly focused on exploiting knowledge from a KG for item recommendations, and it is unclear whether we can exploit the knowledge in the other way, i.e, whether user-item interaction histories can be used for improving the performance of completing the KG with regard to the domain of items. In this paper, we investigate the effect of knowledge transfer between two tasks: (1) item recommendations, and (2) KG completion, via a co-factorization model (CoFM) which can be seen as a transfer learning model. We evaluate CoFM by comparing it to three competitive baseline methods for each task. Results indicate that considering the incompleteness of a KG outperforms other compared methods, including a state-of-the-art factorization method leveraging existing knowledge from the KG. In addition, the results show that exploiting user-item interaction histories also improves the performance of completing the KG with regard to the domain of items, which has not been studied before |
| 关 键 词: | 背景知识; 领域性能; 因子分解 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-12-14：chenjy |
| 最后编审: | 2023-05-11：chenjy |
| 阅读次数: | 22 |