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A mobile services recommendation system fuses implicit and explicit user trust relationships
Journal of Ambient Intelligence and Smart Environments ( IF 1.7 ) Pub Date : 2021-01-18 , DOI: 10.3233/ais-200585
Pengcheng Luo 1, 2 , Jilin Zhang 1, 2, 3 , Jian Wan 1, 2, 4 , Nailiang Zhao 1, 2 , Zujie Ren 5 , Li Zhou 1, 2 , Jing Shen 1, 2, 3
Affiliation  

In recent years, with the development of advanced mobile applications, people’s 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 user’s 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.

中文翻译:

移动服务推荐系统融合了隐式和显式用户信任关系

近年来,随着高级移动应用程序的发展,人们更容易收集人们的各种日常行为数据,例如地理位置,社会信息,兴趣爱好。为了处理这些数据,数据跨边界融合已经成为一项关键技术,并且存在一些挑战,例如解决跨边界业务完整性,跨边界价值互补等问题。移动服务建议要求提高建议的准确性。用户信任是衡量用户之间信息相似性的有效方法。使用信任可以有效提高建议的准确性。现有方法对一般信任数据的利用率低,信任数据稀疏,缺乏用户信任特征。因此,需要提出一种方法来弥补显式信任关系的缺点,并提高用户兴趣特征完成的准确性。本文提出了一种推荐模型,用于从用户数据中挖掘隐式信任关系,并集成用户的显式社会信息。首先,使用传统的奇异值分解(SVD)模型改进了评级预测模型,并从用户的历史数据中挖掘了隐式信任关系。然后,将它们与显式的社会信任关系融合,以获得交叉数据融合模型。我们使用三个不同的数量级对模型进行了测试。我们比较了两种模型的用户偏好预测准确性:一种不整合社交信息,另一种整合社交信息。结果表明,我们的模型提高了用户偏好预测的准确性,并且对冷启动用户具有更高的准确性。在这三个数据集上,平均误差分别降低了2.29%,5.44%和4.42%,表明这是一种有效的数据交叉融合技术。
更新日期:2021-01-20
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