Abstract
Recommender systems can provide users with an ordered list of various items, which greatly assists users to purchase products that they are satisfied with. However, item recommendation has been confronted with some inherent problems, such as sparse ratings and long-tail distribution, resulting in low accuracy of recommendations and insignificant marketing. In this paper, we propose a novel learning model based on trust diffusion and global item (TDGIL) to improve the accuracy of item rating prediction for recommender systems. Specifically, first, the rating information on items is mined and aggregated to the greatest extent based on trust diffusion characteristics among users. The benchmark prediction of item recommendation is updated by a user trust neighbor set and its item ratings, which are obtained by a trust diffusion algorithm. Then, the difference weights and compensation coefficients for all items are defined to learn users’ potential preferences in the proposed global item model. Finally, the TDGIL learning algorithm is presented to train and learn the target networks by random gradient descent. The extensive experiments and results on two real-world datasets demonstrated that our proposed model can achieve significant improvements in the accuracy of rating prediction compared with some state-of-the-art methods.
Highlights
With the explosive growth of Internet information and the emergence of new e-commerce services, users often suffer from information overload [1]
We introduce a novel pipeline to improve the accuracy of item rating prediction in recommendation systems, which first learns the user trust diffusion feature and the difference of global items to jointly improve the benchmark predictor and establish their neural networks
Extensive experiments are conducted on two real datasets to study the performance of the proposed model, and the comparison results show that our TDGIL outperforms other state-of-the-art methods
Summary
With the explosive growth of Internet information and the emergence of new e-commerce services, users often suffer from information overload [1]. Different from conventional similarity recommendation methods, the TDGIL model utilizes the deep learning method to mine more rating information among users to improve the accuracy of item rating prediction. In detail, compared with other methods, the main differences of our proposed learning model are that it can utilize the trust diffusion among users and deep neural network to fully explore available rating records to alleviate the impact of the data sparsity problem on item recommendations. 1. We introduce a novel pipeline to improve the accuracy of item rating prediction in recommendation systems, which first learns the user trust diffusion feature and the difference of global items to jointly improve the benchmark predictor and establish their neural networks.
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