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  3. recommender/processed/venue_features.parquet +3 -0
  4. recommender/raw/dataset_TIST2015_Checkins.txt +3 -0
  5. recommender/raw/dataset_TIST2015_Cities.txt +415 -0
  6. recommender/raw/dataset_TIST2015_POIs.txt +3 -0
  7. recommender/raw/readme.txt +51 -0
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  dataset_TIST2015_Checkins.txt filter=lfs diff=lfs merge=lfs -text
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  dataset_TIST2015_POIs.txt filter=lfs diff=lfs merge=lfs -text
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+ Al-Ahmadi 29.097000 48.068002 KW Kuwait Provincial capital
305
+ Ad Damman 26.333298 50.083300 SA Saudi Arabia Provincial capital
306
+ Al-Muharraq 26.262000 50.616001 BH Bahrain Provincial capital
307
+ Manama 26.216667 50.583340 BH Bahrain National and provincial capital
308
+ Jidd Hafs 26.215956 50.537872 BH Bahrain Provincial capital
309
+ Ar Rifa 26.120997 50.549005 BH Bahrain Provincial capital
310
+ Sitrah 26.100000 50.613850 BH Bahrain Provincial capital
311
+ Riyadh 24.650001 46.770004 SA Saudi Arabia National and provincial capital
312
+ Ajman 25.402860 55.441090 AE United Arab Emirates Provincial capital
313
+ Sharjah 25.371999 55.411003 AE United Arab Emirates Provincial capital
314
+ Doha 25.284357 51.532336 QA Qatar National and provincial capital
315
+ Dubai 25.271001 55.329004 AE United Arab Emirates Provincial capital
316
+ Abu Dhabi 24.476004 54.370999 AE United Arab Emirates National and provincial capital
317
+ Baku 40.323996 49.816000 AZ Azerbaijan National and provincial capital
318
+ Muscat 23.607000 58.588003 OM Oman National and provincial capital
319
+ Delhi 28.666665 77.216662 IN India Provincial capital
320
+ New Delhi 28.568727 77.216752 IN India National capital
321
+ Almaty 43.255062 76.912624 KZ Kazakhstan Provincial capital
322
+ Chennai 13.061997 80.247997 IN India Provincial capital
323
+ Mumbai 19.077003 72.826003 IN India Provincial capital
324
+ Pune 18.535743 73.852273 IN India Other
325
+ Hyderabad 17.395002 78.485005 IN India Provincial capital
326
+ Bangalore 12.974751 77.587732 IN India Other
327
+ Colombo 6.927003 79.848001 LK Sri Lanka National and provincial capital
328
+ Davao 7.080084 125.613685 PH Philippines Other
329
+ Manado 1.483335 124.849999 ID Indonesia Provincial capital
330
+ Hiroshima 34.378002 132.445009 JP Japan Provincial capital
331
+ Shimonoseki 33.949998 130.933297 JP Japan Other
332
+ Chang Rai 19.906002 99.833005 TH Thailand Provincial capital
333
+ Phuket 7.876998 98.385004 TH Thailand Provincial capital
334
+ Banda Aceh 5.566662 95.333338 ID Indonesia Provincial capital
335
+ Ipoh 4.590999 101.076004 MY Malaysia Provincial capital
336
+ Medan 3.584997 98.676001 ID Indonesia Provincial capital
337
+ Pinang 5.364000 100.311004 MY Malaysia Provincial capital
338
+ Kangar 6.433002 100.189999 MY Malaysia Provincial capital
339
+ Alor Setar 6.111000 100.361998 MY Malaysia Provincial capital
340
+ Chiang Mai 18.786996 99.000001 TH Thailand Provincial capital
341
+ Phra Nakhon Si Ayutthaya 14.344002 100.560997 TH Thailand Provincial capital
342
+ Pathum Thani 14.016699 100.533304 TH Thailand Provincial capital
343
+ Nonthaburi 13.848039 100.519858 TH Thailand Provincial capital
344
+ Bangkok 13.750002 100.516672 TH Thailand National and provincial capital
345
+ Samut Prakan 13.588002 100.603999 TH Thailand Provincial capital
346
+ Samut Sakhon 13.536000 100.274005 TH Thailand Provincial capital
347
+ Chon Buri 13.367997 100.987003 TH Thailand Provincial capital
348
+ Nakhon Pathom 13.817997 100.063999 TH Thailand Provincial capital
349
+ Khon Kaen 16.433001 102.836998 TH Thailand Provincial capital
350
+ Nakhon Ratchasima 14.968998 102.102004 TH Thailand Provincial capital
351
+ Udon Thani 17.406999 102.790000 TH Thailand Provincial capital
352
+ Kuantan New Port 3.799998 103.333303 MY Malaysia Other
353
+ Kuala Terengganu 5.325003 103.141000 MY Malaysia Provincial capital
354
+ Kuala Lumpur 3.150000 101.708002 MY Malaysia National and provincial capital
355
+ Shah Alam 3.066696 101.549998 MY Malaysia Provincial capital
356
+ Kuching 1.548999 110.343997 MY Malaysia Provincial capital
357
+ Seremban 2.708001 101.950003 MY Malaysia Provincial capital
358
+ Melaka 2.199996 102.255004 MY Malaysia Provincial capital
359
+ Johor Baharu 1.500003 103.760002 MY Malaysia Provincial capital
360
+ Pekanbaru 0.550998 101.432998 ID Indonesia Provincial capital
361
+ Singapore 1.268856 103.831795 SG Singapore National capital
362
+ Kota Baharu 6.118002 102.240001 MY Malaysia Provincial capital
363
+ Ho Chi Minh City 10.759185 106.662457 VN Vietnam Other
364
+ Taipei 25.035093 121.506724 CN China Provincial capital
365
+ Shanghai 31.248000 121.473001 CN China Provincial capital
366
+ Beijing 39.906189 116.388037 CN China National and provincial capital
367
+ Vladivostok 43.129998 131.960440 RU Russia Provincial capital
368
+ Pusan 35.157996 129.054988 KR South Korea Provincial capital enclave
369
+ Inch`on 37.486242 126.633421 KR South Korea Other
370
+ Seoul 37.542357 126.935245 KR South Korea National capital and provincial capital enclave
371
+ Fukuoka 33.579999 130.401991 JP Japan Provincial capital
372
+ Naha 26.205003 127.692001 JP Japan Provincial capital
373
+ Kowloon 22.379904 114.230116 CN China Other
374
+ Hong Kong 22.274001 114.176998 CN China Other
375
+ Quezon City 14.649993 121.033342 PH Philippines Other
376
+ Manila 14.616702 120.966949 PH Philippines National and provincial capital
377
+ Kota Kinabalu 5.967243 116.087581 MY Malaysia Provincial capital
378
+ Krasnoyarsk 56.016666 92.833336 RU Russia Provincial capital
379
+ Irkutsk 52.317000 104.248000 RU Russia Provincial capital
380
+ Yakutsk 62.010000 129.830005 RU Russia Provincial capital
381
+ Khabarovsk 48.431997 135.127000 RU Russia Provincial capital
382
+ Sapporo 43.055001 141.345991 JP Japan Provincial capital
383
+ Sendai 38.253996 140.891014 JP Japan Provincial capital
384
+ Tokyo 35.683002 139.809007 JP Japan National and provincial capital
385
+ Kawasaki 35.504577 139.727215 JP Japan Other
386
+ Yokohama 35.436996 139.619998 JP Japan Other
387
+ Nagoya 35.154999 136.921006 JP Japan Provincial capital
388
+ Kyoto 35.009001 135.755002 JP Japan Other
389
+ Kobe 34.690149 135.221212 JP Japan Other
390
+ Osaka 34.635996 135.519004 JP Japan Provincial capital
391
+ Gifu 35.409996 136.761994 JP Japan Provincial capital
392
+ Cape Town -33.978996 18.482005 ZA South Africa Provincial capital
393
+ Nairobi -1.269999 36.803998 KE Kenya National and provincial capital
394
+ Johannesburg -26.178957 28.004311 ZA South Africa Provincial capital
395
+ Padang -0.954000 100.351999 ID Indonesia Provincial capital
396
+ Bandung -6.912000 107.606998 ID Indonesia Provincial capital
397
+ Semarang -6.971004 110.426005 ID Indonesia Provincial capital
398
+ Yogyakarta -7.796997 110.368999 ID Indonesia Provincial capital
399
+ Perth -31.956003 115.853005 AU Australia Provincial capital
400
+ Fremantle -32.044167 115.934572 AU Australia Other
401
+ Pontianak -0.023004 109.339003 ID Indonesia Provincial capital
402
+ Palembang -2.990997 104.757004 ID Indonesia Provincial capital
403
+ Tanjungkarang-Telukbetung -5.409000 105.254002 ID Indonesia Provincial capital
404
+ Jakarta -6.166665 106.800004 ID Indonesia National and provincial capital
405
+ Samarinda -0.502002 117.154000 ID Indonesia Provincial capital
406
+ Balikpapan -1.209996 116.859997 ID Indonesia Other
407
+ Bandjermasin -3.328002 114.589000 ID Indonesia Provincial capital
408
+ Surabaja -7.399998 112.684375 ID Indonesia Other
409
+ Mataram -8.589996 116.137000 ID Indonesia Provincial capital
410
+ Denpasar -8.706690 115.217272 ID Indonesia Provincial capital
411
+ Brisbane -27.453996 153.026002 AU Australia Provincial capital
412
+ Sydney -33.889770 151.028200 AU Australia Other
413
+ Adelaide -34.811676 138.520828 AU Australia Provincial capital
414
+ Melbourne -37.852956 145.075105 AU Australia Other
415
+ Auckland -36.903996 174.761002 NZ New Zealand Provincial capital
recommender/raw/dataset_TIST2015_POIs.txt ADDED
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+ size 231769137
recommender/raw/readme.txt ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ This dataset includes long-term (about 18 months from April 2012 to September 2013) global-scale check-in data collected from Foursquare.
2
+ It contains 33,278,683 checkins by 266,909 users on 3,680,126 venues (in 415 cities in 77 countries). Those 415 cities are the most checked 415 cities in the world, each of which contains at least 10000 check-ins). Please see the references for more details about data collection and processing.
3
+ It contains three files in tsv format.
4
+
5
+ - File dataset_TIST2015_Checkins.txt contains all check-ins with 4 columns, which are:
6
+ 1. User ID (anonymized)
7
+ 2. Venue ID (Foursquare)
8
+ 3. UTC time
9
+ 4. Timezone offset in minutes (The offset in minutes between when this check-sin occurred and the same time in UTC, i.e., UTC time + offset is the local time)
10
+
11
+ - File dataset_TIST2015_POIs.txt contains all venue data with 7 columns, which are:
12
+ 1. Venue ID (Foursquare)
13
+ 2. Latitude
14
+ 3. Longitude
15
+ 4. Venue category name (Foursquare)
16
+ 5. Country code (ISO 3166-1 alpha-2 two-letter country codes)
17
+
18
+ - File dataset_TIST2015_Cities.txt contains all 415 cities data with 6 columns, which are:
19
+ Venue category ID (Foursquare)
20
+ 1. City name
21
+ 2. Latitude (of City center)
22
+ 3. Longitude (of City center)
23
+ 4. Country code (ISO 3166-1 alpha-2 two-letter country codes)
24
+ 5. Country name
25
+ 6. City type (e.g., national capital, provincial capital)
26
+
27
+
28
+ =============================================================================================================================
29
+ Please cite our papers if you publish material based on this dataset.
30
+
31
+ =============================================================================================================================
32
+ REFERENCES
33
+
34
+ @article{yang2015nationtelescope,
35
+ title={NationTelescope: Monitoring and visualizing large-scale collective behavior in LBSNs},
36
+ author={Yang, Dingqi and Zhang, Daqing and Chen, Longbiao and Qu, Bingqing},
37
+ journal={Journal of Network and Computer Applications},
38
+ volume={55},
39
+ pages={170--180},
40
+ year={2015},
41
+ publisher={Elsevier}
42
+ }
43
+
44
+ @article{yang2015participatory,
45
+ title={Participatory cultural mapping based on collective behavior in location based social networks},
46
+ author={Yang, Dingqi and Zhang, Daqing and Qu, Bingqing},
47
+ journal={ACM Transactions on Intelligent Systems and Technology},
48
+ year={2015},
49
+ note = {in press},
50
+ publisher={ACM}
51
+ }