A Research On Location Trajectory Mining And Its Application In Mobile Social Networks

Nowadays,location-based services(LBSs)become more and more popular.However,the information provided by conventional LBS that solely relies on positioning equipment is relatively limited.With the development of Mobile Internet and smart phone technology,mobile social networks not only contain user big data,such as friends,interests,social data,etc.,but also precipitate a large amount of user location trajectory data.Location trajectory mining in mobile social networks has important significance.It can be applied to location-based services,mining human activity patterns and behavioral characteristics,and can also be applied to urban computing projects such as intelligent transportation,smart tourism,environmental monitoring,and energy consumption.How to fully exploit the user's location trajectory and the multiple information of the social network to provide users with more convenient,accurate and secure location services is one of the most important issues in the current mobile social network research.Aiming at location trajectory mining and its application in mobile social networks,we wish to establish an efficient and accurate user interest information recommendation and clustering mechanism in LBS to incorporate the user's interest context into the model,xwhich can improve the recommendation and clustering performance based on user characteristics.At the same time,based on the user's location privacy preservation scheme,we hope to achieve the purpose of guaranteeing user's location privacy in mobile social network with confidence.Specifically,we focus on a number of topics in location-based services(LBS),including location recommendation,community detection and privacy protection.In location recommendation,the topic of“Next POI Recommendation "refers to recommend the next point of interest to the users.The difficulty is to make full use of mobile social network data(interest,location,friends relationship,etc.),combining them with recommendation techniques to achieve the purpose of recommending a proper location to the user.In location-based community detection,finding communities based on location interests has significant research value for people to deeply understand the topology and community topics in mobile social networks.The difficulty lies in the fact that mobile social networks have multiple entities such as users and locations,and multiple relationships such as user relationships?user interest relationships and user location relationships.In this complex network,integrating multi-mode heterogeneous entities and relationships is the key of location community detection.In addition,preserving location privacy is a growing concern in location-based services.Only if users'privacy be properly preserved,they are willing to use mobile social networks.The difficulty of location privacy protection is that the information provided by the user and the obtained location information service are contradictory.The less data provided by the user,the worse the quality of the location service that can be obtained.In response to the above problems,our work focuses on the following three aspects:Firstly,for location recommendation in LBS,traditional algorithms failed to capture user interest,location trajectory time dependence and personalized location information at the same time.We propose a recurrent neural network(LSTM)based next POI recommendation method.We empirically observe that user interest,location trajectory,time dependence,and context scenarios have a great impact on user's location recommendation.We analyze and mine the hidden information behind user location trajectory,effectively explore the user's interest,and combine the context information to effectively recommend the next POI for the user.In this work,experiments based on real datasets demonstrate the effectiveness of the proposed approach.Secondly,for community detection in LBS,the difficulty in mining location community in mobile social network lies in the fusion of multi-mode heterogeneous entities and relationships.We propose a deep learning algorithm for location interest community detection.The algorithm uses the topic model to extract user interests,and uses deep learning technology to integrate user relationships,user interest relationships,and user location relationships to detect community.Experimental results verify the effectiveness of the proposed algorithm and demonstrate the cohesiveness of the divided communities in user characteristics.Thirdly,for location privacy preservation in LBS,the traditional algorithms based on the pull strategy had the difficulty to avoid different levels of LBS servers or agents being compromised.We propose to use distributed cache pushing to protect location privacy.The basic idea is to apply distributed caching proxies to store the most popular location-related data and proactively push the data to the user.If the data required by the user is available from the cache,the user does not need to send a location-based query,thereby protecting the privacy of the user.The proposed push-based location privacy protection scheme called LPPS introduces a distributed caching layer to store popular data related to the current location and push it to mobile users.We propose a caching proxy deployment and cache push strategy to achieve k-anonymity of location privacy.In the case of cache misses,we propose a cache replacement and updating strategy to mine the real hot spots hidden in a large number of fake feedback indexes.Simulations based on trajectory data show that the proposed scheme has higher service coverage,better cache hit ratio and lower communication overhead compared to conventional approaches.