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Currently, internet and distance education technologies are rapidly integrating. Learner-centered online learning offers the advantages of flexibility in time and space, affordability, and comprehensive services. However, learners often face the challenge of dealing with an overwhelming amount of learning resources, which is commonly referred to as “information overload.” This paper aims to address the “cold start” problem in the collaborative filtering (CF) algorithm by designing and implementing a personalized learning resource recommendation system. This system incorporates the CF algorithm and introduces the latest learning resource list, popular learning resource list, and user resource tag matching. Additionally, deep learning technology is applied to recommendation systems to tackle issues of data sparsity and information overload. This article studies the characteristics of DeepFM and NeuMF models and embeds them into the model using one hot encoding. DeepFM training results in the model have the lowest Loss function and the highest AUC. In the context of information overload, personalized recommendation services for learning resources in the internet learning environment have become an inevitable trend, with important research significance and wide application value.
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