Improved FC-LFM Algorithm Integrating Time Decay Factor

Zhi-Gang, Gan; Shen, Rao; Jiang, Xiaoning; Yu, Cheng‐Ching; Yu, B. X.; Li, Chuanhuang

doi:10.1007/s13369-021-05637-0

Cited by 2 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the memory-related characteristics of MOOC learners should be incorporated into the model [27]. According to the timeframes of learners' course ratings, the memory retention ratio of a course should be incorporated as a factor in a learner's course ratings.…”

Section: Learners' Course Ratings With Memory Weightmentioning

confidence: 99%

Personalized Hybrid Recommendation Algorithm for MOOCs Based on Learners’ Dynamic Preferences and Multidimensional Capabilities

Liu

2023

Applied Sciences

View full text Add to dashboard Cite

In the MOOCs context, learners experience information overload. Thus, it is necessary to improve personalized recommendation algorithms for learners. The current recommendation algorithm focuses mainly on the learners’ course ratings. However, the choice of courses is not only based on the learners’ interests and preferences. It is also affected by learners’ knowledge domains and learning capabilities, all of which change dynamically over time. Therefore, this study proposes a personalized hybrid recommendation algorithm combining clustering with collaborative filtering. First, data on learners’ course rating preferences, course attribute preferences, and multidimensional capabilities that match course traits are used based on multidimensional item response theory. Second, considering that learners’ preferences and multidimensional capabilities change dynamically over time, the Ebbinghaus forgetting curve is introduced by integrating memory weights to improve the accuracy and interpretation of the proposed recommendation algorithm for MOOCs. Finally, the performance of the proposed recommendation algorithm is investigated using data from Coursera, an internationally renowned MOOCs platform. The experimental results show that the proposed recommendation algorithm is superior to the baseline algorithms. Accordingly, relevant suggestions are proposed for the development of MOOCs.

show abstract

Section: Learners' Course Ratings With Memory Weightmentioning

confidence: 99%

Personalized Hybrid Recommendation Algorithm for MOOCs Based on Learners’ Dynamic Preferences and Multidimensional Capabilities

Liu

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

English Online Course Development Model and Course Content Recommendation

2022

Mobile Information Systems

View full text Add to dashboard Cite

The process of education informatization has been accelerated in the Internet era. English course teaching in colleges and universities also gradually follows the development of the times and makes efforts to promote the reform of informatization teaching. This paper gives the design and application strategy of the English course development model in China’s colleges and universities and designs a diversified teaching model. In addition, aiming at the unreasonable selection of teaching resources and learning materials, this paper designs a multidimensional course recommendation algorithm with integrated features. The proposed algorithm uses a deep learning model for data feature extraction and extracts the information of course attributes and review texts. Then an improved association rule algorithm is conceived to perform association clustering analysis on the original course categories to increase the coverage of recommendations. Experimental results demonstrate that the proposed content recommendation algorithm can recommend high-quality course resources for English teachers, and its performance is better than other algorithms.

show abstract

Improved FC-LFM Algorithm Integrating Time Decay Factor

Cited by 2 publications

References 33 publications

Personalized Hybrid Recommendation Algorithm for MOOCs Based on Learners’ Dynamic Preferences and Multidimensional Capabilities

Personalized Hybrid Recommendation Algorithm for MOOCs Based on Learners’ Dynamic Preferences and Multidimensional Capabilities

English Online Course Development Model and Course Content Recommendation

Contact Info

Product

Resources

About