現代人隨著科學技術的發展改變了生活方式。大多數人依靠網際網路工作、學習知識和傳達信息，這使網際網路成為生活中不可或缺的一部分。 通過搜索引擎查找資訊是一種現代規範。 但是，在過去幾年中，網頁數量顯著增加。 在短時間內找到用戶想要的資訊變得越來越困難。 因此，出現了推薦系統的概念。 推薦系統適用於許多層面，如新聞，電影，音樂，但沒有推薦系統適用於學術文章。 在本文中，除了分析文章之間的相
似性之外，我們還使用不同的語義模型來分析字詞和文章之間的潛在關係。 然後使用分類算法對同一類別中的類似文章進行分類，並將其推薦給用戶。

Modern people change the way of life with the development of science and technology. Most people rely on the Internet to work, learn knowledge and convey messages, which makes the Internet an integral part of life. Finding information through search engines is a modern norm. However, in the past few years, the number of web pages has increased significantly. It is becoming increasingly difficult to find the information that users want in a short period of time. Therefore, a recommendation system has emerged. Recommendation systems are applied at many levels such as news, movies, music, but there is no recommendation system that is suitable for academic articles. In this thesis, we use different semantic models to analyze the potential relationship between terms and articles besides analyzing the similarity between articles. Classification algorithms are then used to classify similar articles in the same category and recommend them to users.

Chapter I. Introduction.....1
Chapter II. Relate Works.....5
2.1 Different kind of semantic models.....5
2.2 Different kind of classification algorithm.....8
Chapter III. The proposed Methodology.....9
3.1 Natural Language Processing.....11
3.1.1 Stemming.....11
3.1.2 Stop-words.....12
3.1.3 Non-words token.....12
3.2 Matrix step.....12
3.3 Semantic models.....13
3.3.1 LSA.....13
3.3.2 PLSA.....14
3.3.3 LDA.....16
3.4 Classification algorithms.....18
3.4.1 K-nearest neighbors (K-NN).....19
3.4.2 Support vector machine.....20
Chapter IV. Experimental analysis and results.....23
4.1 The Dataset and Natural Language Processing .....23
4.2 Experiment results.....24
Chapter V. Conclusion.....37
References.....39

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