1College of Information, Liaoning University, Shenyang 110036, China 2Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University, Changchun 130012, China
[Objective] This paper improves the matrix factorization algorithm with neighboring user’s comments, aiming to address the sparse comments issue and improve recommendation accuracy. [Methods] First, we used the Multi-layer Perceptron to improve the matrix decomposition algorithm and obtain the deep nonlinear features of users and commodities. Then, we processed the reviews and integrated the characteristics of users and their neighbors. Third, we identified users’ features in line with their preferences. Finally, we made recommendations based on the obtained prediction scores of the features. [Results] We compared the performance of our new algorithm with other models on the Amazon dataset. The accuracy, recall, and normalized cumulative loss gain of the proposed model increased by up to 8.3%, 22.8%, and 14.9%, respectively. [Limitations] We neither included the time factor of the user’s comments, nor excluded the fake comments. [Conclusions] Our new algorithm could effectively improve the recommendation results.
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