Distance Education Evaluation: An Analysis of the β Factor from LV Model Subjectivity

Leite, Eliana Alves Moreira; Machado, Ana Rita Marinho; Sales, Gilvandenys Leite; Zednik, Herik; Maia, Silvania

doi:10.1007/978-3-319-16486-1_122

Cited by 2 publications

(1 citation statement)

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“…Users who have common interests in the same course may give different ratings to the course. At the same time, referring to the famous Ebbinghaus forgetting curve, a nonlinear logistic function is designed for the algorithm to explore the user's rating forgetting rule more closely, so as to give each normalized rating a different time forgetting weight [14][15][16]. Considering the influence of the recommendation and the authenticity of the neighbor set, this paper sets an effective weight factor when calculating the user similarity, which helps to improve the accuracy of the recommendation result [17].…”

Section: Algorithm Proposedmentioning

confidence: 99%

[Retracted] Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model

Zhang

2022

Journal of Mathematics

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Based on the understanding and comparison of various main recommendation algorithms, this paper focuses on the collaborative filtering algorithm and proposes a collaborative filtering recommendation algorithm with improved user model. Firstly, the algorithm considers the score difference caused by different user scoring habits when expressing preferences and adopts the decoupling normalization method to normalize the user scoring data; secondly, considering the forgetting shift of user interest with time, the forgetting function is used to simulate the forgetting law of score, and the weight of time forgetting is introduced into user score to improve the accuracy of recommendation; finally, the similarity calculation is improved when calculating the nearest neighbor set. Based on the Pearson similarity calculation, the effective weight factor is introduced to obtain a more accurate and reliable nearest neighbor set. The algorithm establishes an offline user model, which makes the algorithm have better recommendation efficiency. Two groups of experiments were designed based on the mean absolute error (MAE). One group of experiments tested the parameters in the algorithm, and the other group of experiments compared the proposed algorithm with other algorithms. The experimental results show that the proposed method has better performance in recommendation accuracy and recommendation efficiency.

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Section: Algorithm Proposedmentioning

confidence: 99%