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PSO-weighted random forest for attractive tourism spots recommendation
Future Generation Computer Systems  (IF7.187),  Pub Date : 2021-09-28, DOI: 10.1016/j.future.2021.09.029
Yuran Zhang, Ziyan Tang

To accelerate searching an enjoyable holiday resort from massive-scale tourism spots on social media, the TR-DNNMF tourism spots recommendation system combining neural network and matrix decomposition is developed in this work. The model is built upon the architecture of the neural collaborative filtering model. It leverages the generalized matrix decomposition model coupled with the multi-layer neural network model as the pre-training model for training. Subsequently, it integrates the two models to anticipate user’s rating of tourism spots and further recommends the suitable tourism spots. Our model combines the linearity of matrix factorization and the nonlinearity of deep neural network to uncover the potential features of user-scenic spots. This makes the model exhibit high scalability and strong fitting ability. Experiments are conducted based on the domestic tourist attractions from user interaction data. Features that is non-descriptive to the model are discarded to reduce the time and memory consumption. Conclusively, our designed system can offer satisfactory prediction, as evidenced by the competitive accuracies on six experimental data sets.