Artificial neural networks have been widely applied to spatial modeling and knowledge discovery because of their high-level intelligence and flexibility. Their highly parallel and distributed structure makes them inherently suitable for parallel computing. As the technology of parallel and high-performance computing evolves and computing resources become more widely available, new opportunities exist for spatial neural network models to benefit from this advancement in terms of better handling computational and data intensity associated with spatial problems. In this study, we present a hybrid parallel ensemble neural network approach for modeling spatial land-use change. Our approach combines the shared-memory paradigm and the embarrassingly parallel method by leveraging the power of multicore computer clusters. The efficacy of this approach is demonstrated by the parallelization of Fuzzy ARTMAP neural network models, which have been extensively used in land-use modeling applications. We adopt an ensemble structure of neural networks to train multiple models in parallel and make use of the entire dataset simultaneously. We evaluate the proposed parallelization approach by examining performance variation of training datasets with alternative sizes. Experimental results reveal great potential of higher performance achievement when our hybrid parallel computing approach is applied to large spatial modeling problems.
{"title":"Parallelization of ensemble neural networks for spatial land-use modeling","authors":"Zhaoya Gong, Wenwu Tang, J. Thill","doi":"10.1145/2442796.2442808","DOIUrl":"https://doi.org/10.1145/2442796.2442808","url":null,"abstract":"Artificial neural networks have been widely applied to spatial modeling and knowledge discovery because of their high-level intelligence and flexibility. Their highly parallel and distributed structure makes them inherently suitable for parallel computing. As the technology of parallel and high-performance computing evolves and computing resources become more widely available, new opportunities exist for spatial neural network models to benefit from this advancement in terms of better handling computational and data intensity associated with spatial problems. In this study, we present a hybrid parallel ensemble neural network approach for modeling spatial land-use change. Our approach combines the shared-memory paradigm and the embarrassingly parallel method by leveraging the power of multicore computer clusters. The efficacy of this approach is demonstrated by the parallelization of Fuzzy ARTMAP neural network models, which have been extensively used in land-use modeling applications. We adopt an ensemble structure of neural networks to train multiple models in parallel and make use of the entire dataset simultaneously. We evaluate the proposed parallelization approach by examining performance variation of training datasets with alternative sizes. Experimental results reveal great potential of higher performance achievement when our hybrid parallel computing approach is applied to large spatial modeling problems.","PeriodicalId":107369,"journal":{"name":"Workshop on Location-based Social Networks","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128059577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Byoungjip Kim, Youngki Lee, Sang Jeong Lee, Yunseok Rhee, Junehwa Song
As location-aware mobile devices such as smartphones have now become prevalent, people are able to easily record their trajectories in daily lives. Such personal trajectories are a very promising means to share their daily life experiences, since important contextual information such as significant locations and activities can be extracted from the raw trajectories. In this paper, we propose MetroScope, a trajectory-based real-time and on-the-go experience sharing system in a metropolitan city. MetroScope allows people to share their daily life experiences through trajectories, and enables them to refer to other people's diverse and interesting experiences in a city. Eventually, MetroScope aims to satisfy users' ever-changing interest in their social environments and enrich their life experiences in a city. To achieve real-time, on-the-go, and personalized recommendation, we propose an approach of monitoring activity patterns over people's location streams.
{"title":"Towards trajectory-based experience sharing in a city","authors":"Byoungjip Kim, Youngki Lee, Sang Jeong Lee, Yunseok Rhee, Junehwa Song","doi":"10.1145/2063212.2063221","DOIUrl":"https://doi.org/10.1145/2063212.2063221","url":null,"abstract":"As location-aware mobile devices such as smartphones have now become prevalent, people are able to easily record their trajectories in daily lives. Such personal trajectories are a very promising means to share their daily life experiences, since important contextual information such as significant locations and activities can be extracted from the raw trajectories. In this paper, we propose MetroScope, a trajectory-based real-time and on-the-go experience sharing system in a metropolitan city. MetroScope allows people to share their daily life experiences through trajectories, and enables them to refer to other people's diverse and interesting experiences in a city. Eventually, MetroScope aims to satisfy users' ever-changing interest in their social environments and enrich their life experiences in a city. To achieve real-time, on-the-go, and personalized recommendation, we propose an approach of monitoring activity patterns over people's location streams.","PeriodicalId":107369,"journal":{"name":"Workshop on Location-based Social Networks","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124858371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There has been considerable interest in applying social network analysis methods to geographically embedded networks such as population migration and international trade. However, research is hampered by a lack of support for exploratory spatial-social network analysis in integrated tools. To bridge the gap, this research introduces a spatial-social network visualization tool, the GeoSocialApp, that supports the exploration of spatial-social networks among network, geographical, and attribute spaces. It also supports exploration of network attributes from community-level (clustering) to individual-level (network node measures). Using an international trade case study, this research shows that mixed methods --- computational and visual --- can enable discovery of complex patterns in large spatial-social network datasets in an effective and efficient way.
{"title":"Spatial-social network visualization for exploratory data analysis","authors":"W. Luo, A. MacEachren, Peifeng Yin, F. Hardisty","doi":"10.1145/2063212.2063216","DOIUrl":"https://doi.org/10.1145/2063212.2063216","url":null,"abstract":"There has been considerable interest in applying social network analysis methods to geographically embedded networks such as population migration and international trade. However, research is hampered by a lack of support for exploratory spatial-social network analysis in integrated tools. To bridge the gap, this research introduces a spatial-social network visualization tool, the GeoSocialApp, that supports the exploration of spatial-social networks among network, geographical, and attribute spaces. It also supports exploration of network attributes from community-level (clustering) to individual-level (network node measures). Using an international trade case study, this research shows that mixed methods --- computational and visual --- can enable discovery of complex patterns in large spatial-social network datasets in an effective and efficient way.","PeriodicalId":107369,"journal":{"name":"Workshop on Location-based Social Networks","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126787124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents methods for annotating georeferenced photos with descriptive tags, exploring the annotations for other georeferenced photos which are available at online repositories like Flickr. Specifically, by using the geospatial coordinates associated to the photo which we want to annotate, we start by collecting the photos from an online repository which were taken from nearby locations. Next, and for each tag associated to the collected photos, we compute a set of relevance estimators with basis on factors such as the tag frequency, the geospatial proximity of the photo, the image content similarity, and the number of different users employing the tag. The multiple estimators can then be combined through supervised learning to rank methods such as Rank-Boost or AdaRank, or through unsupervised rank aggregation methods well-known in the information retrieval literature, namely the CombSUM or the CombMNZ approaches. The most relevant tags are finally suggested. Experimental results with a collection of photos collected from Flickr attest for the adequacy of the proposed approaches.
{"title":"Tag recommendation for georeferenced photos","authors":"Ana Silva, Bruno Martins","doi":"10.1145/2063212.2063229","DOIUrl":"https://doi.org/10.1145/2063212.2063229","url":null,"abstract":"This paper presents methods for annotating georeferenced photos with descriptive tags, exploring the annotations for other georeferenced photos which are available at online repositories like Flickr. Specifically, by using the geospatial coordinates associated to the photo which we want to annotate, we start by collecting the photos from an online repository which were taken from nearby locations. Next, and for each tag associated to the collected photos, we compute a set of relevance estimators with basis on factors such as the tag frequency, the geospatial proximity of the photo, the image content similarity, and the number of different users employing the tag. The multiple estimators can then be combined through supervised learning to rank methods such as Rank-Boost or AdaRank, or through unsupervised rank aggregation methods well-known in the information retrieval literature, namely the CombSUM or the CombMNZ approaches. The most relevant tags are finally suggested. Experimental results with a collection of photos collected from Flickr attest for the adequacy of the proposed approaches.","PeriodicalId":107369,"journal":{"name":"Workshop on Location-based Social Networks","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123490184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Twitter presents a source of information that cannot easily be obtained anywhere else. However, though many posts on Twitter reveal up-to-the-minute information about events in the world or interesting sentiments, far more posts are of no interest to the general audience. A method to determine which Twitter users are posting reliable information and which posts are interesting is presented. Using this information a search through a large, online news corpus is conducted to discover future events before they occur along with information about the location of the event. These events can be identified with a high degree of accuracy by verifying that an event found in one news article is found in other similar news articles, since any event interesting to a general audience will likely have more than one news story written about it. Twitter posts near the time of the event can then be identified as interesting if they match the event in terms of keywords or location. This method enables the discovery of interesting posts about current and future events and helps in the identification of reliable users.
{"title":"Identification of live news events using Twitter","authors":"Alan Jackoway, H. Samet, Jagan Sankaranarayanan","doi":"10.1145/2063212.2063224","DOIUrl":"https://doi.org/10.1145/2063212.2063224","url":null,"abstract":"Twitter presents a source of information that cannot easily be obtained anywhere else. However, though many posts on Twitter reveal up-to-the-minute information about events in the world or interesting sentiments, far more posts are of no interest to the general audience. A method to determine which Twitter users are posting reliable information and which posts are interesting is presented. Using this information a search through a large, online news corpus is conducted to discover future events before they occur along with information about the location of the event. These events can be identified with a high degree of accuracy by verifying that an event found in one news article is found in other similar news articles, since any event interesting to a general audience will likely have more than one news story written about it. Twitter posts near the time of the event can then be identified as interesting if they match the event in terms of keywords or location. This method enables the discovery of interesting posts about current and future events and helps in the identification of reliable users.","PeriodicalId":107369,"journal":{"name":"Workshop on Location-based Social Networks","volume":"5 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130123452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most people usually drive their familiar routes to work and are concerned about the traffic on their way to work. If a driver's preferred route is known, the traffic congestion information on his/her way to work will be reported in time. However, the current navigation systems focus on planning the shortest path or the fastest path from a given start point to a given destination point. In this paper, we present a novel personalized route planning framework that considers user movement behaviors. The proposed framework comprises two components, familiar road network construction and route planning. In the first component, we mine familiar road segments from a driver's historical trajectory dataset, and construct a familiar road network. For the second component, we propose an efficient route planning algorithm to generate the top-k familiar routes given a start point and a destination point. We evaluate the performance of our algorithm using a real dataset, and compare our algorithm with an existing approach in terms of effectiveness and efficiency.
{"title":"Discovering personalized routes from trajectories","authors":"Kai-Ping Chang, Ling-Yin Wei, Mi-Yen Yeh, Wen-Chih Peng","doi":"10.1145/2063212.2063218","DOIUrl":"https://doi.org/10.1145/2063212.2063218","url":null,"abstract":"Most people usually drive their familiar routes to work and are concerned about the traffic on their way to work. If a driver's preferred route is known, the traffic congestion information on his/her way to work will be reported in time. However, the current navigation systems focus on planning the shortest path or the fastest path from a given start point to a given destination point. In this paper, we present a novel personalized route planning framework that considers user movement behaviors. The proposed framework comprises two components, familiar road network construction and route planning. In the first component, we mine familiar road segments from a driver's historical trajectory dataset, and construct a familiar road network. For the second component, we propose an efficient route planning algorithm to generate the top-k familiar routes given a start point and a destination point. We evaluate the performance of our algorithm using a real dataset, and compare our algorithm with an existing approach in terms of effectiveness and efficiency.","PeriodicalId":107369,"journal":{"name":"Workshop on Location-based Social Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116294995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the growing number of smartphones and increasing interest of location-based social network, check-in becomes more and more popular. Check-in means a user has visited a location, e.g., a Point of Interest (POI). The category of the POI implies the activities which can be conducted. In this paper, we are trying to discover the categories of the POIs in which users are being located (i.e., activities) based on GPS reading, time, user identification and other contextual information. However, in the real world, a single user's data is often insufficient for training individual activity recognition model due to limited check-ins each day. Thus we study how to collaboratively use similar users' check-in histories to train Conditional Random Fields (CRF) to provide better activity recognition for each user. We leverage k-Nearest Neighbors (kNN) and Hierarchical Agglomerative Clustering (HAC) for clustering similar users and learn a separated CRF for each cluster on the histories of its users. As for similarity, the first metric involves linear combination of three types of user factors attained by matrix decomposition on User-Activity, User-Temporal and User-Transition matrices. The second metric between two clusters can be the cosine similarity between weights of CRF corresponding to these two clusters. By the initial experiment on real world check-in data from Dianping, we show that it is possible to improve the classifier performance through collaboration and that the first similarity metric is not good to find the real neighbors.
{"title":"Collaborative activity recognition via check-in history","authors":"Defu Lian, Xing Xie","doi":"10.1145/2063212.2063230","DOIUrl":"https://doi.org/10.1145/2063212.2063230","url":null,"abstract":"With the growing number of smartphones and increasing interest of location-based social network, check-in becomes more and more popular. Check-in means a user has visited a location, e.g., a Point of Interest (POI). The category of the POI implies the activities which can be conducted. In this paper, we are trying to discover the categories of the POIs in which users are being located (i.e., activities) based on GPS reading, time, user identification and other contextual information. However, in the real world, a single user's data is often insufficient for training individual activity recognition model due to limited check-ins each day. Thus we study how to collaboratively use similar users' check-in histories to train Conditional Random Fields (CRF) to provide better activity recognition for each user. We leverage k-Nearest Neighbors (kNN) and Hierarchical Agglomerative Clustering (HAC) for clustering similar users and learn a separated CRF for each cluster on the histories of its users. As for similarity, the first metric involves linear combination of three types of user factors attained by matrix decomposition on User-Activity, User-Temporal and User-Transition matrices. The second metric between two clusters can be the cosine similarity between weights of CRF corresponding to these two clusters. By the initial experiment on real world check-in data from Dianping, we show that it is possible to improve the classifier performance through collaboration and that the first similarity metric is not good to find the real neighbors.","PeriodicalId":107369,"journal":{"name":"Workshop on Location-based Social Networks","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125514746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The advent of location-based social networking sites provides an open sharing space of crowd-sourced lifelogs that can be regarded as a novel source to monitor massive crowds' lifestyles in the real world. In this paper, we challenge to analyze urban characteristics in terms of crowd behavior by utilizing the crowd lifelogs in urban area. In order to collect crowd behavioral data, we utilize Twitter where enormous numbers of geo-tagged crowd's micro lifelogs can be easily acquired. We model the crowd behavior on the social network sites as a feature, which will be used to derive crowd-based urban characteristics. Based on this crowd behavior feature, we analyze significant crowd behavioral patterns for extracting urban characteristics. In the experiment, we actually conduct the urban characterization over the crowd behavioral patterns using a large number of geo-tagged tweets found in Japan from Twitter and report a comparison result with map-based observation of cities as an evaluation.
{"title":"Crowd-based urban characterization: extracting crowd behavioral patterns in urban areas from Twitter","authors":"Shoko Wakamiya, Ryong Lee, K. Sumiya","doi":"10.1145/2063212.2063225","DOIUrl":"https://doi.org/10.1145/2063212.2063225","url":null,"abstract":"The advent of location-based social networking sites provides an open sharing space of crowd-sourced lifelogs that can be regarded as a novel source to monitor massive crowds' lifestyles in the real world. In this paper, we challenge to analyze urban characteristics in terms of crowd behavior by utilizing the crowd lifelogs in urban area. In order to collect crowd behavioral data, we utilize Twitter where enormous numbers of geo-tagged crowd's micro lifelogs can be easily acquired. We model the crowd behavior on the social network sites as a feature, which will be used to derive crowd-based urban characteristics. Based on this crowd behavior feature, we analyze significant crowd behavioral patterns for extracting urban characteristics. In the experiment, we actually conduct the urban characterization over the crowd behavioral patterns using a large number of geo-tagged tweets found in Japan from Twitter and report a comparison result with map-based observation of cities as an evaluation.","PeriodicalId":107369,"journal":{"name":"Workshop on Location-based Social Networks","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129303381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Wirz, P. Schläpfer, M. Kjærgaard, D. Roggen, S. Feese, G. Tröster
Detecting pedestrians moving together through public spaces can provide relevant information for many location-based social applications. In this work we present an online method to detect such pedestrian flocks by spatio-temporal clustering of location trajectories. Compared to prior work, our method provides increased robustness against the influence of noisy and missing GPS data often encountered in urban environments. To assess the performance of the method, we record GPS trajectories from ten subjects walking through a city. The data set contains various flock formations and corresponding ground truth information is available. With this data set, we can evaluate the accuracy of our method to detect flocks. Results show that we can detect flocks and their members with an accuracy of 91.3%. We evaluate the influence of noisy and missing location data on the detection accuracy and show that the introduced filtering heuristics provides increased detection accuracy in such realistic situations.
{"title":"Towards an online detection of pedestrian flocks in urban canyons by smoothed spatio-temporal clustering of GPS trajectories","authors":"M. Wirz, P. Schläpfer, M. Kjærgaard, D. Roggen, S. Feese, G. Tröster","doi":"10.1145/2063212.2063220","DOIUrl":"https://doi.org/10.1145/2063212.2063220","url":null,"abstract":"Detecting pedestrians moving together through public spaces can provide relevant information for many location-based social applications. In this work we present an online method to detect such pedestrian flocks by spatio-temporal clustering of location trajectories. Compared to prior work, our method provides increased robustness against the influence of noisy and missing GPS data often encountered in urban environments. To assess the performance of the method, we record GPS trajectories from ten subjects walking through a city. The data set contains various flock formations and corresponding ground truth information is available. With this data set, we can evaluate the accuracy of our method to detect flocks. Results show that we can detect flocks and their members with an accuracy of 91.3%. We evaluate the influence of noisy and missing location data on the detection accuracy and show that the introduced filtering heuristics provides increased detection accuracy in such realistic situations.","PeriodicalId":107369,"journal":{"name":"Workshop on Location-based Social Networks","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116963035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Symeonidis, Alexis Papadimitriou, Y. Manolopoulos, P. Senkul, I. H. Toroslu
Social networks have evolved with the combination of geographical data, into Geo-social networks (GSNs). GSNs give users the opportunity, not only to communicate with each other, but also to share images, videos, locations, and activities. The latest developments in GSNs incorporate the usage of location tracking services, such as GPS to allow users to "check in" at various locations and record their experience. In particular, users submit ratings or personal comments for their location/activity. The vast amount of data that is being generated by users with GPS devices, such as mobile phones, needs efficient methods for its effective management. In this paper, we have implemented an online prototype system, called Geo-social recommender system, where users can get recommendations on friends, locations and activities. For the friend recommendation task, we apply the FriendLink algorithm, which performs a local path traversal on the friendship network. In order to provide location/activity recommendations, we represent data by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) technique. As more data is accumulated to the system, we use incremental solutions to update our tensor. We perform an experimental evaluation of our method with two real data sets and measure its effectiveness through recall/precision.
{"title":"Geo-social recommendations based on incremental tensor reduction and local path traversal","authors":"P. Symeonidis, Alexis Papadimitriou, Y. Manolopoulos, P. Senkul, I. H. Toroslu","doi":"10.1145/2063212.2063228","DOIUrl":"https://doi.org/10.1145/2063212.2063228","url":null,"abstract":"Social networks have evolved with the combination of geographical data, into Geo-social networks (GSNs). GSNs give users the opportunity, not only to communicate with each other, but also to share images, videos, locations, and activities. The latest developments in GSNs incorporate the usage of location tracking services, such as GPS to allow users to \"check in\" at various locations and record their experience. In particular, users submit ratings or personal comments for their location/activity. The vast amount of data that is being generated by users with GPS devices, such as mobile phones, needs efficient methods for its effective management. In this paper, we have implemented an online prototype system, called Geo-social recommender system, where users can get recommendations on friends, locations and activities. For the friend recommendation task, we apply the FriendLink algorithm, which performs a local path traversal on the friendship network. In order to provide location/activity recommendations, we represent data by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) technique. As more data is accumulated to the system, we use incremental solutions to update our tensor. We perform an experimental evaluation of our method with two real data sets and measure its effectiveness through recall/precision.","PeriodicalId":107369,"journal":{"name":"Workshop on Location-based Social Networks","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114552551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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