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。
Abstract
This paper summarized four types of recommendation-related user information from micro-blog systemthe 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.
Key wordslearning to rank; user recommendation; micro-blog.
关键词
排序学习 /
用户推荐 /
微博
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Key words
learning to rank /
user recommendation /
micro-blog.
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参考文献
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