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| 基于排序学习的微博用户推荐 | |
| Alternative Title | Micro-blog User Recommendation Using Learning to Rank |
| 彭泽环; 孙乐; 韩先培; 石贝 | |
| 2013 | |
| Source | 中文信息学报
 |
| ISSN | 1003-0077 |
| Volume | 27Issue:4Pages:96-102 |
| English Abstract | 该文在分析总结影响微博用户推荐的四大类信息,包括用户的内容信息、个人信息、交互信息和社交拓扑信息的基础上,提出一个基于排序学习的微博用户推荐框架,排序学习的本质是用机器学习中的分类或回归方法解决排序问题,该框架可以综合各类信息特征进行用户推荐.实验结果表明:(1)融合多个特征综合推荐通常可以取得更好的推荐效果;(2)基于用户个人信息、交互信息、社交拓扑信息的推荐效果均好于基于用户内容的推荐效果. |
| Indexed Type | CSCD |
| Abstract | This paper summarized four types of recommendation-related user information from micro-blog system: the user content(UC),the personal information(PI),the interaction(IA)and the social topological information (ST).Based on the four types of information,a user recommendation framework using learning-to-rank technology is built in the paper.Experiment results show:(1)using several features to recommend usually get a better result than using a single feature;(2)recommendation performance based on UC,PI,IA respectively is better than that based on UC. |
| Keyword | 排序学习 用户推荐 微博 Learning To Rank User Recommendation Micro-blog |
| Department | 中国科学院 软件研究所基础软件中心,北京,100190 |
| Language | 中文 |
| CSCD ID | CSCD:4907560 |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16850 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | 彭泽环,孙乐,韩先培,等. 基于排序学习的微博用户推荐[J]. 中文信息学报,2013,27(4):96-102. |
| APA | 彭泽环,孙乐,韩先培,&石贝.(2013).基于排序学习的微博用户推荐.中文信息学报,27(4),96-102. |
| MLA | 彭泽环,et al."基于排序学习的微博用户推荐".中文信息学报 27.4(2013):96-102. |
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