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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Liang Zhang Xiumin Liu Xiujuan Liu |
| Copyright Year | 2008 |
| Description | Author affiliation: Inf. & Eng. Sch., Hangzhou Normal Univ., Hangzhou (Liang Zhang; Xiumin Liu) || Math. & Inf. Eng. Sch., Jiaxing Coll., Jiaxing (Xiujuan Liu) |
| Abstract | A novel personalized instructing recommendation system (PIRS) is designed for Web-based learning. This system recognizes different patterns of learning style and Web using habits through testing the learning styles of students and mining their Web browsing logs. Firstly, it processes the sparse data by item-based top-N recommendation algorithm in the course of testing the learning styles. Then it analyzes the habits and the interests of the Web users through mining the frequent sequences in the Web browsing logs by AprioriAll algorithm. Finally, this system completes personalized recommendation of the learning content based on the learning style and the habit of Web usage. Experiment shows that the recommendation model, proposed in this paper, is not only satisfied with the urgent need of the users, but also feasible and effective. |
| Starting Page | 2517 |
| Ending Page | 2521 |
| File Size | 239731 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780769533988 |
| DOI | 10.1109/ICYCS.2008.115 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-11-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | AprioriAll Algorithm Psychology Data mining Association rules Web Mining Personalized Recommendation Design engineering Collaboration Web pages Web mining Filtering algorithms Learning Style Internet Testing |
| Content Type | Text |
| Resource Type | Article |
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