CAJ | 학술논문

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。
재현유적추천계통중，기우용호흥취모형도능구표체출용호적흥취，단재용호흥취발생변화시각불능구급시경신모형。제출기우용호반궤내용래실시경신용호흥취적소식추천계통，통과실시경신모형화특정향량진이득도용호당전최필배적추천결과。병사용HBase（Hadoop Database）작위존저，능경호지괄응수거규모적증장。
The user interests models in the existing recommendation systems can express the user's interests effectively, but they can'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.