Point-of-interest recommendation using extended random walk with restart on geographical-temporal hybrid tripartite graph

Figures & data

Table 1. An example of visiting history.

User	Visited POI	Time window (TW)
U1	POI1	3–6 PM
U1	POI2	3–6 PM
U1	POI3	6–9 PM
U2	POI3	6–9 PM
U2	POI4	6–9 PM

Figure 1. A geographical-temporal hybrid tripartite graph.

Table 2. Datasets specification.

Dataset	# Users	#Locations	#Check-ins	$\mathbf{A}\mathbf{V}{\mathbf{G}}_{\mathbf{u}}$	$\mathbf{A}\mathbf{V}{\mathbf{G}}_{\mathbf{l}}$
Weeplaces	570	3025	12815	22.48	4.24
Gowalla	246	7746	34051	138.42	4.39

Table 3. Session node extraction at different time windows.

Dataset	6-hour	12-hour	24-hour
Weeplaces	76	111	120
Gowalla	851	988	1520

Figure 2. Performance of GHTG-ERWR algorithm in locations recommendation at different time windows for (a) Weeplaces and (b) Gowalla datasets.

Figure 3. Performance of GHTG-ERWR algorithm in different number of recommended locations for (a) Weeplaces and (b) Gowalla datasets.

Figure 4. Comparison of GHTG-ERWR and RWR-HST algorithms for the Weeplaces datasets (a) Precision of top-N and (b) Recall of top-N. For the Gowalla dataset (c) Precision of top-N, (d) Recall of top-n.

Figure 5. Comparison of the performance of GHTG-ERWR and RWR-HST algorithms in term of F-score for (a) Weeplaces and (b) Gowalla.