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中文摘要：
在现有的推荐系统中,基于用户兴趣模型都能够表达出用户的兴趣,但在用户兴趣发生变化时却不能够及时更新模型.提出基于用户反馈内容来实时更新用户兴趣的消息推荐系统,通过实时更新模型和特征向量进而得到用户当前最匹配的推荐结果.并使用HBase（Hadoop Database）作为存储,能更好地适应数据规模的增长.
英文摘要：
The user interests models in the existing recommendation systems can express the user＆#39;s interests effectively, but they can＆#39;t update the user interests models in time while the user interests change. This paper proposes a recommendation system of messages depending on user’s feedback to modify user interests. It can update model and the feature vector in time, and then the most matching recommendation results for every user. In addition, the system can better adapt to the growth of the scale of data by using HBase （Hadoop Database） as storage.
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