Research Area:  Machine Learning

Abstract:

In recent years, with the development of advanced mobile applications, peoples various daily behavior data, such as geographic location, social information, hobbies, are more easily collected. To process these data, data cross-boundary fusion has become a key technology, and there are some challenges, such as solving the problems of the cross-boundary business integrity, cross-boundary value complementarity and so on. Mobile Services Recommendation requires improved recommendation accuracy. User trust is an effective measure of information similarity between users. Using trust can effectively improve the accuracy of recommendations. The existing methods have low utilization of general trust data, sparseness of trust data, and lack of user trust characteristics. Therefore, a method needs to be proposed to make up for the shortcomings of explicit trust relationships and improve the accuracy of user interest feature completion. In this paper, a recommendation model is proposed to mine the implicit trust relationships from user data and integrate the explicit social information of users. First, the rating prediction model was improved using the traditional Singular Value Decomposition (SVD) model, and the implicit trust relationships were mined from the users historical data. Then, they were fused with the explicit social trust relationships to obtain a crossover data fusion model. We tested the model using three different orders of magnitude. We compared the user preference prediction accuracies of two models: one that does not integrate social information and one that integrates social information. The results show that our model improves the user preference prediction accuracy and has higher accuracy for cold start users. On the three data sets, the average error is reduced by 2.29%, 5.44% and 4.42%, suggesting that it is an effective data crossover fusion technology.

Keywords:  
Mobile Services
Recommendation System
Trust Relationships
Singular Value Decomposition
Deep Learning
Machine Learning

Author(s) Name:   Luo Pengchenga; Zhang Jilina; Wan Jiana ; Zhao Nailianga; Ren Zujiee; Zhou Lia; Shen Jinga.

Publisher name:  IOS PRESS

DOI:  10.3233/AIS-200585

Volume Information:  vol. 13, no. 1, pp. 21-35, 2021

Paper Link:   https://content.iospress.com/articles/journal-of-ambient-intelligence-and-smart-environments/ais200585