Fused semantic information and hierarchical attention network for course recommendation

Tieyuan Liu; Rupeng Zhou; Chuangying Zhu; Liang Chang

doi:10.1117/12.2685504

21 July 2023 Fused semantic information and hierarchical attention network for course recommendation

Tieyuan Liu, Rupeng Zhou, Chuangying Zhu, Liang Chang

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 1271732 (2023) https://doi.org/10.1117/12.2685504
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

With the development of smart education, personalized course recommendations are getting more and more attention. Recently, some in-depth learning-based solutions have been proposed. The main principle is to make sequential recommendations based on the learner's historical learning records, rarely use the semantic information covered by the course profile and user profile to complete the recommendation task. User preferences change over time. To solve these problems, a course recommendation model (FIHA) that integrates semantic information and hierarchical attention network is proposed. next class. Specifically, TextCNN is used to perform semantic processing on the course introduction to generate vectors, and then the learner's historical access course records are used to capture the learner's long-term and short-term preferences using hierarchical attention mechanism, and the learner's dynamic preference representation is obtained. The two are fused and recommendations are made based on their scores to courses. Experiments on real datasets show that this method is superior to other mainstream advanced methods.

Citation Download Citation

Tieyuan Liu, Rupeng Zhou, Chuangying Zhu, and Liang Chang "Fused semantic information and hierarchical attention network for course recommendation", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 1271732 (21 July 2023); https://doi.org/10.1117/12.2685504

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Machine learning

Semantics

Data modeling

Feature extraction

Matrices

Tunable filters

Convolution

Show All Keywords

Keywords/Phrases

Search In:

Publication Years