Co-spatial Searcher: Efficient Tag-Based Collaborative Spatial Search on Geo-social Network

Abstract

The proliferation of geo-social network, such as Foursquare and Facebook Places, enables users to generate location information and its corresponding descriptive tags. Using geo-social networks, users with similar interests can plan for social activities collaboratively. This paper proposes a novel type of query, called Tag-based top-k Collaborative Spatial (TkCoS) query, for users to make outdoor plans collaboratively. This type of queries aim to retrieve groups of geographic objects that can satisfy a group of users’ requirements expressed in tags, while ensuring that the objects be within the minimum spatial distance from the users. To answer TkCoS queries efficiently, we introduce a hybrid index structure called Spatial-Tag R-tree (STR-tree), which is an extension of the R-tree. Based on STR-tree, we propose a query processing algorithm that utilizes both spatial and tag similarity constraints to prune search space and identify desired objects quickly. Moreover, a differential impact factor is adopted to fine-tune the returned results in order to maximize the users’ overall satisfaction. Extensive experiments on synthetic and real datatsets validate the efficiency and the scalability of the proposed algorithm.