Academic Journal of Computing & Information Science, 2021, 4(2); doi: 10.25236/AJCIS.2021.040209.
School of Information, Engineering Nanjing University of Finance and Economics, Nanjing, China
Although collaborative filtering recommendation has been very mature in practical application, the problem of data sparsity has been difficult to solve. Sparse data seriously weakens the accuracy of similarity measurement, and also affects the recommendation accuracy of recommendation system. Therefore, this paper makes a deep study on this problem and proposes an improved similarity measure algorithm based on matrix factorization filling and filling confidence. The core idea of the algorithm is to fill the original sparse data set first, and then calculate the similarity based on the filling. In the process of matrix filling, considering that the matrix decomposition model can effectively alleviate the sparsity of data by associating users and items with hidden features, this paper chooses the BiasSVD matrix decomposition model as the basis for filling improvement. Considering that the value of user's interest or behavior will decline with the change of time, the user's recent interaction data can better reflect their current interest than the long-term interaction data, so the improved algorithm integrates the time decline function into the BiasSVD algorithm when filling, and uses the time decline function to give different weights to the user's historical data at different stages, In order to achieve better filling effect. At the same time, after filling, considering that the traditional matrix filling algorithm ignores the credibility difference between the real data and the filled data, and there is no distinction in the next step of similarity calculation, which affects the performance of the recommendation. Therefore, the concept of filling confidence is introduced in the calculation of similarity, which fully distinguishes the reference of the real score and the predicted score.
collaborative filtering; data sparsity; user similarity measure; data filling
Liu Xiaoyu. Improved Algorithm of Similarity Measure Based on Matrix Factorization Filling and Filling Confidence. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 2: 49-54. https://doi.org/10.25236/AJCIS.2021.040209.
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