| Summary: | 碩士 === 國立中正大學 === 資訊工程研究所 === 103 === Nowadays, a vast amount of information is freely available on the internet. How to acquire needed and interesting information rapidly in the network is an important issue. In this thesis we design and implement a news recommendation system that can recommend users interesting news according to their preference derived from their behavior. In addition, it also recommend hot key terms in each category of news source.
In order to get the preference of users, our system collects the browsing behavior of users, extracts key terms in each news article that has been read, and perform statistical analysis to derive the interested set of key terms as user preference. The keyword extraction is based on the TF-IDF technique. As the news articles sometimes may contain new terms not existing in the dictionary, we need to find a way to generate new terms. We use punctuation block extraction method and N-gram to generate new terms.
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