Application of Top-N Rule-based Optimal Recommendation System for Language Education Content based on Parallel Computing

In recent years personalized recommendation services have been applied to many areas of society, typically in the fields of e-commerce, short videos and so on. In response to the serious performance problems of the current online language education platform content recommendation, so in the face of the above opportunities and challenges, this paper designs a new online English education model to allow university students to get a full and more three-dimensional training of English language learning. Based on the MU platform, this paper obtains data from the platform and uses crawler technology to sample and standardize the learning resources for online education. Then user information, such as explicit and implicit ratings of courses, is selected as the main basis for training a user interest preference model. Immediately afterwards, a PRF algorithm combining data parallelism and task parallelism optimization was executed and implemented on Apeche Spark to provide some optimization of data accuracy and content recommendation methods. Finally, the top-N recommendation rule is used to propose a dynamic evolutionary process of identifying students’ preferences or learning habits through the results of previous data analysis, so as to make more accurate course content recommendations and learning content guidance for students’ English learning. The online three-dimensional teaching model proposed in this paper focuses more on time-series research than traditional algorithms, and can more accurately capture the dynamic changes in students’ learning abilities.

@article{Hu2023,
  title     = {Application of Top-N Rule-based Optimal Recommendation System for Language Education Content based on Parallel Computing},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {6},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Nan Hu},
  doi       = {10.14569/IJACSA.2023.01406110},
  url       = {https://doi.org/10.14569/IJACSA.2023.01406110}
}