Case study on application of personalized recommendation system in online exhibition event: Focusing on industry-academic cooperation EXPO

Byun, Moon-Kyoung; Lee, Jung‐Hoon

doi:10.9728/dcs.2021.22.4.655

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…The system generates customer-related recommendations by analyzing customer characteristics and similarities in preferences. The system enhances the accuracy of product recommendations and improves customer satisfaction [16]. Lv proposed an interpretable recommendation model based on the existing recommendation system.…”

Section: Related Workmentioning

confidence: 99%

“…Solving the user characteristic matrix and the item characteristic matrix of the target space of the TUF model is the main task of the iterative algorithm. The goal of this algorithm is to achieve or complete the optimal solution of the model [11][12][13][14][15][16][17][18][19][20][21][22]. There are multiple methods to solve the TUF model, and out of these algorithms, the Wiberg algorithm demonstrates superior performance.…”

Section: Operational Procedures Of the Tuf Model Solving Algorithmmentioning

confidence: 99%

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A Collaborative Filtering Recommendation Algorithm Model Based on User Feature Transfer

Han

2023

Int. J. Emerg. Technol. Learn.

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The quality of teaching resources and recommended practices in flipped classrooms determine the advantages and disadvantages of this educational model. However, current teaching resource recommendation platforms suffer from issues such as cold startup and sparse data. In order to address these issues, a collaborative filtering recommendation algorithm based on user feature transfer was proposed and utilized to examine the correlation between students’ preference behavior and the recommendation of teaching resources in flipped classes for tourism management. The aim was to enhance the quality of teaching in current classes. Using this algorithm, a transfer by user feature (TUF) model is constructed. In this model, a soft embedding method is employed to accurately extract user feature information in the auxiliary space. This method addresses the challenge of filling large-scale missing matrices in the auxiliary space. Additionally, singular value decomposition is performed to obtain more precise user features. Finally, an optimized Wiberg algorithm with rapid convergence is designed to solve the constructed model. The performance of this algorithm is measured and compared using two common datasets. Compared to other algorithms, the proposed algorithm demonstrates superior performance and a lower mean absolute error value. In summary, the recommendation algorithm can enhance the quality of resource organization and provide more suitable personalized recommendations for the recommendation platform. This is beneficial for improving the quality of students’ learning.

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Section: Related Workmentioning

confidence: 99%

Section: Operational Procedures Of the Tuf Model Solving Algorithmmentioning

confidence: 99%

A Collaborative Filtering Recommendation Algorithm Model Based on User Feature Transfer

Han

2023

Int. J. Emerg. Technol. Learn.

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“…The traditional education system is still the main mechanism of learning, but it has been unable to meet people's demands for personalized resources. In this context, the personalized multimodal multimedia course resource-sharing platform has developed rapidly in recent years (Byun & Lee, 2021).…”

Section: Introductionmentioning

confidence: 99%

A Personalized Course Resource Recommendation Method Based on Deep Learning in an Online Multi-Modal Multimedia Education Cloud Platform

Zhang

2023

International Journal of Information Technologies and Systems Approach

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Aiming at the problem that unstructured text in online multi-modal multimedia education is easy to cause error propagation, this paper proposes a personalized course resource recommendation method using deep learning in online multi-modal multimedia education cloud platform. First, the word vector of the text is obtained from the course data set by using the BERT pre-training model, and its semantic information in different contexts is analyzed. Then, the more complex representation of each word is extracted through the long short-term memory network (LSTM), in which the multi-head attention layer adds different weights to different word vector to better capture the key information in the sentence. Finally, the CRF layer is used to identify sentence entities, and the Sigmoid layer is used to extract relations, thus completing personalized course resource recommendation, which is significantly improved compared with other models. Experimental analysis shows that the algorithm is effective in personalized course resource recommendation.

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Distributed Hybrid Recommendation Algorithm in News Dissemination Innovation System

Liu

2022

Application of Intelligent Systems in Multi-Modal Information Analytics

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Case study on application of personalized recommendation system in online exhibition event: Focusing on industry-academic cooperation EXPO

Cited by 6 publications

References 11 publications

A Collaborative Filtering Recommendation Algorithm Model Based on User Feature Transfer

A Collaborative Filtering Recommendation Algorithm Model Based on User Feature Transfer

A Personalized Course Resource Recommendation Method Based on Deep Learning in an Online Multi-Modal Multimedia Education Cloud Platform

Distributed Hybrid Recommendation Algorithm in News Dissemination Innovation System

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