Abstract
Nowadays, as the number of items is increasing and the number of items that users have access to is limited, user-item preference matrices in recommendation systems are always sparse. This leads to a data sparsity problem. The latent factor analysis (LFA) model has been proposed as the solution to the data sparsity problem. As the basis of the LFA model, the singular value decomposition (SVD) model, especially the biased SVD model, has great recommendation effects in high-dimensional sparse (HiDs) matrices. However, it has the disadvantage of requiring several iterations before convergence. Besides, the model PID-incorporated SGD-based LFA (PSL) introduces the principle of discrete PID controller into the stochastic gradient descent (SGD), the learning algorithm of the SVD model. It could solve the problem of slow convergence speed, but its accuracy of recommendation needs to be improved. In order to make better solution, this paper fuses the PSL model with the biased SVD model, hoping to obtain better recommendation result by combining their advantages and reconciling their disadvantages. The experiments show that this biased PSL model performs better than the traditional matrix factorization algorithms on different sizes of datasets.
Highlights
Aiming to devise better solution to the data sparsity problem, this paper describes a recommendation model based on the latent factor analysis (LFA) model, which is a kind of model-based recommendation techniques of the collaborative filtering (CF) recommendation system [9]
This method adds these two parameters to improve the performance of the recommendation model. Since it assumes that everyone has the same rating standard and all items have the same quality, its recommendation accuracy has to be improved. Since both of them have advantages and disadvantage, this paper introduces the principle of the biased singular value decomposition (SVD) model to the PID-incorporated stochastic gradient descent (SGD)-based LFA (PSL) model to improve the recommendation effect
The biased SVD formula can take into account the scoring habits of different people and the different qualities of items, i.e., taking into account the individuation, to give the most appropriate score for users and items
Summary
It is difficult to satisfy the needs of most users by allowing them to go through the filter by themselves or by filtering only with item labels. In this case, it becomes essential to design a specific recommendation system to help users find the things they might be enthusiastic about. The introduction of the recommendation system can significantly enhance the user’s experience with the software as they can effortlessly meet their own needs. It has great commercial value in the fields of advertising promotion and commodity sales
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