A New Online Education Personalized Recommendation Algorithm

Abstract

For online education platforms, a personalized recommendation system is crucial, and the collaborative filtering algorithm is the primary recommendation algorithm used. This study took the recommendation of crowdfunding platforms as a sample, and enhanced the collaborative filtering algorithm based on the user score and project attribute features of the crowdfunding platform, intending to resolve the cold start issue brought on by the platform's reliance on a single data source. The study concludes with experimental proof of the paper's suggested better method. This approach can alleviate the cold start issue to some degree. The prediction accuracy has been much enhanced in comparison with the conventionally advised method. The method can also adapt to user tastes over time, learning what they like and what they don't. It also has an excellent real-time suggestion impact. The performance verification of the algorithm in this research is also conducted using data from a live crowdfunding site, lending credence to the study's claim of greater practicality.