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Title:
基于排序学习的微博用户推荐
Alternative Title: Micro-blog User Recommendation Using Learning to Rank
Author: 彭泽环 ; 孙乐 ; 韩先培 ; 石贝
Keyword: 排序学习 ; 用户推荐 ; 微博 ; learning to rank ; user recommendation ; micro-blog
Source: 中文信息学报
Issued Date: 2013
Volume: 27, Issue:4, Pages:96-102
Indexed Type: CSCD
Department: 中国科学院 软件研究所基础软件中心,北京,100190
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.
English 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.
Language: 中文
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/16850
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
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,2013-01-01,27(4):96-102.
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