Intelligent recommendation model of tourist places based on collaborative filtering and user preferences

Figures & data

Figure 1. General framework of travel recommendation system.

Figure 2. Collaborative filtering technology flow.

Figure 3. Algorithm flow.

Table 1. Distribution of experimental data.

ID	Training set	Test set
Number of users	2933	733
Number of attractions	188	47
Number of ratings	18902	4726
Sparsity grade	0.963

Figure 4. Comparison of MAE for different similarity measures.

Figure 5. Comparison of RMSE for different similarity measures.

Table 2. MAE corresponding to different degrees of similarity.

Number of neighbors	COS	JMSD	MCF	Proposed
10	0.53437	0.51259	0.46796	0.47645
20	0.52441	0.52415	0.46341	0.45244
40	0.52445	0.53879	0.46712	0.44586
60	0.52751	0.5479	0.47239	0.44681
80	0.53124	0.55459	0.47819	0.44961
100	0.53512	0.55954	0.48327	0.45275

Table 3. Algorithm error comparison.

Method	Average MAE	Error Comparison/%	Minimum MAE	Error Comparison/%
COS	0.52894	14.61	0.52365	14.89
JMSD	0.54227	16.72	0.51259	13.05
MCF	0.47235	4.39	0.46341	3.82

Figure 6. Impact of the preferred strategy on the algorithm.

Data Availability Statement

The data used in the research are available from the author upon request.