Course recommendation based on semantic similarity analysis
| Autor: | Hualong Ma, Yunjun Lu, Xiande Wang, Jianfeng Hou |
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| Rok vydání: | 2017 |
| Předmět: |
Schedule
Vocabulary Information retrieval Computer science media_common.quotation_subject 02 engineering and technology Recommender system Semantics Semantic similarity 020204 information systems Similarity (psychology) ComputingMilieux_COMPUTERSANDEDUCATION 0202 electrical engineering electronic engineering information engineering Selection (linguistics) 020201 artificial intelligence & image processing Cluster analysis media_common |
| Zdroj: | 2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE). |
| Popis: | For many students, course recommendation is a troublesome problem. To choose satisfying courses when they only have a little of information about the courses, they have to turn to common course schedule systems for help, but the result is disappointing. To solve the problem, the method of scoring similarity of courses and clustering courses based on course descriptions is presented. Different from the method, this paper applies semantic similarity analysis into course selection, realizes a course recommendation system. Each course description is first modeled as a document, and a cleantokenize-TFIDF-Word2Vec-Doc2Vec pipeline is built to create vectors for each course from which cosine similarities will be calculated. The result is even better than the above method through evaluation. |
| Databáze: | OpenAIRE |
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