Research On Spatial Group Preference Queries Over Location-Based Social Networks

With the rapid development of online social networking and Location-Based Services(LBS),providing personalized services for users has become a hotspot in recent years,which combining with behavior preferences and locations of different users in social networks.Research on location-based social networks appeared.Location-based social networks contain information about historical behavior preferences of users and ratings of merchant stores.How to effectively use such information to provide users with satisfactory services is one core issue in current location-based social networks.Based on the development of location-based social networks,in this article,we define a new query-spatial group preference query and propose an efficient query processing algorithm.The query,at the background of location-based social network,returns the set of POIs(such as restaurants,hotels.etc.)that meet the preferences requirements of different usersin group.scores and also considering POI ratings and interactions among POIs.Spatial group preference query is of great significance meeting places selecting and task allocation of crowdsourcing games.First,we propose a spatial group preference query algorithm based on user preferences and poi matching,and then we extend the algorithm to solve the problem with semantic extension.In general,the main work and contributions of this article are summarized as follows:(1)Spatial group preference query based on user preference and poi matching.Aiming at the group preference problem in location-based social networks,the spatial group preference query isdefined,combining users' current locations and POI preferences.The calculation formula of user group satisfaction is designed,and a query processing algorithm OPAwhich based on pruning strategies is proposed.This article aslo proposes an index structurecalled CR-tree,with POI semantic category tags and other additional information.The OPC algorithm is designedto further improve the query performance.Experimental results on large-scale datasets show the effectiveness of OPA and OPC algorithms.(2)Spatial group preference query algorithm based on semantic extension.Neither the OPA nor the OPC algorithm does not consider semantic relevance(e.g.,similarity between query terms and POI category terms).Thus,we further study the spatial group preference query problem based on semantic extension and propose the spatial group preference query algorithm OPS based on semantic extension.In the OPS algorithm,we establish a semantic tree to describe the hierarchical relationship between differentdescriptions of POIs.In addition,we improve the longest common subsequence LCSS algorithm to measure the semantic similarity between POIs based on semantic tree.The semantic similarityof POIsis taken as an important factor tomeasure the satisfaction of queries.At the same time,we design and implement the SR-tree index withPOI spatial attributes and semantic attributes to ensure efficient execution of query and return reasonable query results.A variety of pruning strategies are used to filter POI.Experiments on two real datasets in Guangzhou and Shanghai demonstrate the effectiveness of the OPS algorithm.