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An Empirical Recommendation Framework to Support Location-Based Services ?

机译：支持基于位置的服务的经验推荐框架？

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The rapid growth of Global Positioning System (GPS) and availability of real-time Geo-located data allow the mobile devices to provide information which leads towards the Location Based Services (LBS). The need for providing suggestions to personals about the activities of their interests, the LBS contributing more effectively to this purpose. Recommendation system (RS) is one of the most effective and efficient features that has been initiated by the LBS. Our proposed system is intended to design a recommendation system that will provide suggestions to the user and also find a suitable place for a group of users and it is according to their preferred type of places. In our work, we propose the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for clustering the check-in spots of the user’s and user-based Collaborative Filtering (CF) to find similar users as we are considering constructing an interest profile for each user. We also introduced a grid-based structure to present the Point of Interest (POI) into a map. Finally, similarity calculation is done to make the recommendations. We evaluated our system on real world users and acquired the F-measure score on average 0.962 and 0.964 for a single user and for a group of user respectively. We also observed that our system provides effective recommendations for a single user as well as for a group of users.
机译：全球定位系统（GPS）的快速增长和实时地质定位数据的可用性允许移动设备提供引导朝向基于位置的服务（LBS）的信息。需要向个人提供建议，了解他们的利益活动，洛博斯为此目的贡献更有效。推荐系统（RS）是LBS发起的最有效和最有效的功能之一。我们所提出的系统旨在设计一个推荐系统，该系统将向用户提供建议，并为一组用户找到合适的位置，并根据他们的首选地点。在我们的工作中，我们提出了基于密度的空间聚类与噪声（DBSCAN）算法的应用程序，用于聚类用户和基于用户的协作过滤（CF）的签入点，以查找类似用户，因为我们考虑构建兴趣每个用户的配置文件。我们还介绍了基于网格的结构，将兴趣点（POI）呈现为地图。最后，完成了相似性计算来提出建议。我们在真实世界中评估了我们的系统，并分别为单个用户和一组用户获得了平均0.962和0.964的F测量分数。我们还观察到，我们的系统为单个用户提供了有效的建议，以及一组用户提供了有效的建议。

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