基于排序学习的微博用户推荐

摘要

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.%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.