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Federated heterogeneous model and algorithm for personal travel recommendation(PDF)


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Federated heterogeneous model and algorithm for personal travel recommendation
YOU Lin-lin1 HE Jun-shu1 CHEN Kun-xu1 HE Jia-qi1 YUAN Shao-xin2 ZHAO Juan-juan3 CAI Ming1
(1. School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen 518107, Guangdong, China; 2. School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China; 3. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China)
intelligent transportation travel recommendation federated learning mixed logit model privacy protection personalization
To achieve personal travel recommendations by considering both the preference heterogeneity and data privacy, based on the model parametric aggregation and distributed training supported by the federated learning(FL)computing paradigm, a federated mixed Logit(FMXL)model was proposed by decoupling the standard mixed Logit model to separate the parameter estimation of local individual preferences and global population differences. In order to eliminate the dependence of the model on the original data, two federated Gibbs sampling algorithms, to be standardized or aggregated, were designed, to achieve the hierarchical estimation of the model through the interaction of local and global parameters. The proposed model and algorithm were validated for offline and online travel recommendation scenarios based on Swiss Metro data. Analysis results show that for offline scenario, the FMXL model based on two federated Gibbs sampling algorithms increase the log-likelihood value by 157.8 and 153.2, and the prediction rate by 12.3% and 12.1%, respectively, compared with the standard multinomial Logit model. In addition, the computation time reduce by 64.2% and 76.9%, respectively, and the communication times both reduce by 86.2% compared with the mixedLogit model based on the centralized Gibbs sampling algorithm. For the online scenario, both the log-likelihood value and the prediction rate of the FMXL model show an increasing trend, and the computation and communication times of the whole estimation process are lower than those of the standard mixed Logit model. Overall, with the data privacy as the precondition, the federated training of the MXL model ensures the accuracy of travel recommendations and effectively enhances the timeliness of travel recommendations by fully utilizing the idle computing power on the user side, reflecting the high adaptability and scalability of the proposed model and algorithms. In addition, the intelligent progress of the transportation system can be promoted effectively by personal travel recommendations based on the FMXL model. 3 tabs, 4 figs, 30 refs.


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Last Update: 2023-11-10