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Dual-track feedback aggregation recommendation model for programming training
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AI
DOI：10.1016/j.knosys.2022.110087.png)
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Abstract
En 中文
Programming online judges (POJs) are widely used to train programming skills, and exercise recom-mendation algorithms in POJs have attracted wide attention. The current programming recommen-dation algorithms cannot make full use of the feedback of user-item pairs and cannot effectively express students' mastery of exercises. Therefore, we propose a dual-track feedback aggregation recommendation model for programming training (DTFARec). In this model, multiple types of feedback fusion mechanism (MTFFM) and dual-track method (DTM) are proposed to solve this problem and can better express students' mastery of exercises. The MTFFM uses an attention mechanism to learn different feedback information, and the DTM is able to fuse information from both feedback and interactive aspects. The experimental results on a real-world dataset show that the model has better recommendation performance than the best performing benchmark and that our method can effectively model students' mastery of exercises.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Recommendation
Embedding propagation
Online judge
Graph neural network
Journal
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Citations:
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