Data for: A Location-Based Orientation-Aware Recommender System Using IoT Smart Devices and Social Networks

In this study, 20 recommendation lists are provided for each of 7,214 user's locations in total. Four different buffer sizes (two, five, ten, and 15 km) and four UPDs are considered for each user's location to prepare the event recommendation lists. In the implemented scenario, three different scenarios were applied in order to support research claims. The word "SC" refers to a specific scenario as "SC_1" refers to the first scenario. Totally, considering four circle buffers and four UPDs in each buffer, 16 buffer lines will be prepared as "sc_1_line_1" refers to the first UPD in the buffer size of two kilometers. In addition, "sc_1_cycle" refers to all the event venues that are located in the circle buffer size of two kilometers. "EventVenues" and "venuesMBBs" contain geospatial information for the selected venues and also their extracted minimum bounding boxes. All the imported building located in the city of Calgary, Alberta is in the "calgaryBuilding" table, and "userLocations" shows the spatial information considered for the 7,214 user's locations.

本研究总计为7214个用户位置各生成20条活动推荐列表。为生成活动推荐列表，本研究针对每个用户位置设定了四种缓冲区尺度（2公里、5公里、10公里与15公里）以及四个UPD（UPD）参数。在本次研究的实现场景中，为支撑研究主张的验证，共设置了三种不同的实验场景。术语"SC"用于指代特定场景，例如"SC_1"即代表第一个实验场景。综上，若为每个圆形缓冲区配置四个UPD，则总计可生成16条缓冲区数据行，例如"sc_1_line_1"代表2公里缓冲区尺度下的第一个UPD。此外，"sc_1_cycle"指代所有位于2公里圆形缓冲区内的活动场地。"EventVenues"与"venuesMBBs"分别存储了选定活动场地的地理空间信息，以及提取得到的最小外接矩形（minimum bounding boxes, MBBs）。所有导入的加拿大阿尔伯塔省卡尔加里市建筑数据均存储于"calgaryBuilding"数据表中，而"userLocations"表则记录了本次研究用到的7214个用户位置的空间信息。