Optimized Collaborative Filtering Algorithm Based on Item Rating Prediction

2012 ◽  
Author(s):  
Ye Weichuan ◽  
Lin Kunhui ◽  
Zhang Leilei ◽  
Deng Xiang
Keyword(s):  
Collaborative Filtering ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Rating Prediction

scholarly journals An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 68301-68310 ◽  
Author(s):  
Dionisis Margaris ◽  
Anna Kobusinska ◽  
Dimitris Spiliotopoulos ◽  
Costas Vassilakis
Keyword(s):  
Social Network ◽  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Rating Prediction

scholarly journals Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Tongyan Li ◽  
Yingxiang Li ◽  
Chen Yi-Ping Phoebe
Keyword(s):  
Collaborative Filtering ◽  
Sparse Data ◽  
Current Data ◽  
Prediction Algorithm ◽  
Filtering Algorithm ◽  
Recommendation Algorithms ◽  
Collaborative Filtering Algorithm ◽  
Scoring Matrix ◽  
Rating Prediction ◽  
User Ratings

Current data has the characteristics of complexity and low information density, which can be called the information sparse data. However, a large amount of data makes it difficult to analyse sparse data with traditional collaborative filtering recommendation algorithms, which may lead to low accuracy. Meanwhile, the complexity of data means that the recommended environment is affected by multiple dimensional factors. In order to solve these problems efficiently, our paper proposes a multidimensional collaborative filtering algorithm based on improved item rating prediction. The algorithm considers a variety of factors that affect user ratings; then, it uses the penalty to account for users’ popularity to calculate the degree of similarity between users and cross-iterative bi-clustering for the user scoring matrix to take into account changes in user’s preferences and improves on the traditional item rating prediction algorithm, which considers user ratings according to multidimensional factors. In this algorithm, the introduction of systematic error factors based on statistical learning improves the accuracy of rating prediction, and the multidimensional method can solve data sparsity problems, enabling the strongest relevant dimension influencing factors with association rules to be found. The experiment results show that the proposed algorithm has the advantages of smaller recommendation error and higher recommendation accuracy.

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