Collaborative Filtering Recommendation Algorithm
Collaborative filtering algorithm is the most frequently employed algorithm in recommender systems, it is primarily subdivided into user-based collaborative filtering and item-based collaborative filtering (Goldberg et al., 2001). The principle of user-based collaborative filtering is to recommend to the target items that the target user likes but has not bought, whereas the principle of item-based collaborative filtering is to suggest to the target items that are similar to the items the target user likes (Jain et al., 2020; Ajaegbu, 2021). On e-learning websites, the cost of computing the user similarity matrix is typically significantly greater than the cost of calculating the course similarity matrix, making the item-based collaborative filtering recommendation algorithm more efficient.
Li & Ye (2020) modified the conventional recommendation algorithm according to collaborative filtering so that the results of course recommendation are more aligned with the user's interests, thereby significantly enhancing the recommendation’s accuracy and efficiency. Chen et al. (2020) suggested a new course-recommendation algorithm that employs collaborative filtering to facilitate student decision-making. This algorithm employs an enhanced cosine similarity based on a student's previous course selections to enhance the accuracy of the recommendation task and meet the requirements of the users. Wu et al. (2021) proposed a collaborative filtration recommendation model built on deep learning for art and MOOC resources. The model employs embedding vectors that utilizes meta-path context for learning. Second, an attention mechanism is added to promote the interpretability of the recommendation results, and then the relational network information is effectively integrated by suggesting the Laplacian matrix into the a priori distribution of the implicit factor feature matrices. Compared to the conventional approach utilizing scoring matrices, the model utilizing the text-word vector is superior. Li et al. (2020) proposed the Bayesian personalized ranking network (BPRN), which trains to match a user's course preferences with their enrollment history. Experiments indicate that the BPRN framework outperforms contemporary item-based course recommendation methods. Collaborative filtering-based recommendation algorithms are commonly used in recommendation systems, but they encounter several issues. These include cold start, data sparsity, insufficient feature extraction, poor scalability, new item and user problems, user preference drift, and lack of flexibility. Despite improvements, further research is needed to address these challenges and enhance the accuracy and personalization of recommendation algorithms.