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14
Recommending Friends and Locations Based on Individual Location History
, 2008
"... The increasing availability of location-acquisition technologies (GPS, GSM networks, etc.) enables people to log the location histories with spatio-temporal data. Such real-world location histories imply to some extent users ‟ interests in places, and bring us opportunities to understand the correla ..."
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Cited by 67 (14 self)
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The increasing availability of location-acquisition technologies (GPS, GSM networks, etc.) enables people to log the location histories with spatio-temporal data. Such real-world location histories imply to some extent users ‟ interests in places, and bring us opportunities to understand the correlation between users and locations. In this article, we move towards this direction, and report on a personalized friend & location recommender for the geographical information systems (GIS) on the Web. First, in this recommender system a particular individual‟s visits to a geospatial region in the real world are used as their implicit ratings on that region. Second, we measure the similarity between users in terms of their location histories, and recommend each user a group of potential friends in a GIS community. Third, we estimate an individual‟s interests in a set of unvisited regions by involving his/her location history and those of other users. Some unvisited locations that might match their tastes can be recommended to the individual. A framework, referred to as a hierarchicalgraph-based similarity measurement (HGSM), is proposed to uniformly model each individual‟s location history, and effectively measure the similarity among users. In this framework, we take into account three factors: 1) the sequence property of people‟s outdoor movements, 2) the visited popularity of a geospatial region and 3) the hierarchical property of geographic spaces. Further, we incorporated a content-based method into a user-based collaborative filtering algorithm, which uses HGSM as the user similarity measure, to estimate
Learning travel recommendation from user-generated GPS trajectories
- ACM Transaction on Intelligent Systems and Technologies (ACM TIST
"... The advance of GPS-enabled devices facilitates people to record their location histories with GPS traces, which imply human behaviors and preferences related to travel. In this paper, we perform two types of travel recommendations by mining multiple users ’ GPS traces. The first is a generic one tha ..."
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Cited by 46 (9 self)
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The advance of GPS-enabled devices facilitates people to record their location histories with GPS traces, which imply human behaviors and preferences related to travel. In this paper, we perform two types of travel recommendations by mining multiple users ’ GPS traces. The first is a generic one that recommends a user with top interesting locations and travel sequences in a given geospatial region. Here, interesting locations mean the culturally important places, such as Tiananmen Square in Beijing, and frequented public areas, like shopping malls and restaurants. The second is a personalized recommendation that provides an individual with locations matching her travel preferences. To achieve the first recommendation, we model multiple users ’ location histories with a tree-based hierarchical graph (TBHG). Based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual’s access on a location as a directed link from the user to that location. This model infers two values, the interest level of a location and a user’s travel experience, by taking into account 1) the mutual-reinforcement relation between the two values and 2) the geo-region conditions. Considering the inferred values, we mine the classical travel sequences among locations. In the personalized recommendation, we first understand the correlation among locations in terms of 1) the sequences that the locations have been visited and 2) the travel experiences of the persons accessing these locations. Beyond the geo-distance relation, this correlation represents the relation between locations in the
Finding similar users using category-based location history
- In GIS
, 2010
"... In this paper, we aim to estimate the similarity between users according to their GPS trajectories. Our approach first models a user’s GPS trajectories with a semantic location history (SLH), e.g., shopping malls � restaurants � cinemas. Then, we measure the similarity between different users ’ SLHs ..."
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Cited by 21 (5 self)
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In this paper, we aim to estimate the similarity between users according to their GPS trajectories. Our approach first models a user’s GPS trajectories with a semantic location history (SLH), e.g., shopping malls � restaurants � cinemas. Then, we measure the similarity between different users ’ SLHs by using our maximal travel match (MTM) algorithm. The advantage of our approach lies in two aspects. First, SLH carries more semantic meanings of a user’s interests beyond low-level geographic positions. Second, our approach can estimate the similarity between two users without overlaps in the geographic spaces, e.g., people living in different cities. We evaluate our method based on a real-world GPS dataset collected by 109 users in a period of 1 year. As a result, SLH-MTM outperforms the related works [4].
Urban Computing: Concepts, Methodologies, and Applications
"... Urbanization’s rapid progress has modernized many people’s lives, but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities, e.g., traffic flow, human mobility and geo ..."
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Cited by 14 (7 self)
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Urbanization’s rapid progress has modernized many people’s lives, but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities, e.g., traffic flow, human mobility and geographical data. Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people’s lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology, in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Secondly, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety & security, presenting representative scenarios in each category. Thirdly, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we outlook the
Tutorial on Location-Based Social Networks
- In proceeding of International conference on World Wide Web
"... This paper is an abstract of a tutorial on location-based social networks (LBSNs), introducing the concept, unique features, and research philosophy of LBSNs. The slide deck of this tutorial can be found on ..."
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Cited by 7 (1 self)
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This paper is an abstract of a tutorial on location-based social networks (LBSNs), introducing the concept, unique features, and research philosophy of LBSNs. The slide deck of this tutorial can be found on
LORE: Exploiting Sequential Influence for Location Recommendations
"... Providing location recommendations becomes an importan-t feature for location-based social networks (LBSNs), since it helps users explore new places and makes LBSNs more prevalent to users. In LBSNs, geographical in
uence and social in
uence have been intensively used in location rec-ommendations ba ..."
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Cited by 6 (3 self)
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Providing location recommendations becomes an importan-t feature for location-based social networks (LBSNs), since it helps users explore new places and makes LBSNs more prevalent to users. In LBSNs, geographical in
uence and social in
uence have been intensively used in location rec-ommendations based on the facts that geographical proxim-ity of locations significantly affects users ’ check-in behaviors and social friends often have common interests. Although human movement exhibits sequential patterns, most current studies on location recommendations do not consider any se-quential in
uence of locations on users ’ check-in behaviors. In this paper, we propose a new approach called LORE to exploit sequential influence on location recommendations. First, LORE incrementally mines sequential patterns from location sequences and represents the sequential patterns as a dynamic Location-Location Transition Graph (L2TG). LORE then predicts the probability of a user visiting a loca-tion by Additive Markov Chain (AMC) with L2TG. Finally, LORE fuses sequential in
uence with geographical in
uence and social in
uence into a unified recommendation frame-work; in particular the geographical influence is modeled as two-dimensional check-in probability distributions rather than one-dimensional distance probability distributions in existing works. We conduct a comprehensive performance evaluation for LORE using two large-scale real data sets col-lected from Foursquare and Gowalla. Experimental result-s show that LORE achieves significantly superior location recommendations compared to other state-of-the-art recom-mendation techniques.
Cometogether: Discovering communities of places in mobility data
- In MDM
, 2012
"... Abstract—We analyse urban mobility and public places under a new perspective: how can we feature the places in a city based on how people move among them? To answer this question we need to combine places, like points of interest, with mobility information like the trajectories of individuals moving ..."
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Cited by 2 (0 self)
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Abstract—We analyse urban mobility and public places under a new perspective: how can we feature the places in a city based on how people move among them? To answer this question we need to combine places, like points of interest, with mobility information like the trajectories of individuals moving within a city. To accomplish this, we propose a methodology based on complex network analysis: we build a network of points of inter-ests by connecting places by the individual trajectories passing through them. From such network we compute communities finding groups places highly connected by the mobility of the individuals. We present a case study on real trajectory dataset on the city of Milan, showing a complementary view on the urban mobility that is not covered by the state-of-the art techniques on mobility analysis. I.
LBSNRank: Personalized PageRank on Location-based Social Networks ∗
"... Different from traditional social networks, the location-based social networks allow people to share their locations according to location-tagged user-generated contents, such as checkins, trajectories, text, photos, etc. In location-based social networks, which are based on users ’ checkins, people ..."
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Different from traditional social networks, the location-based social networks allow people to share their locations according to location-tagged user-generated contents, such as checkins, trajectories, text, photos, etc. In location-based social networks, which are based on users ’ checkins, people could share his or her location according to checkin while visiting around. However, people’s locations change frequently and the rankings of people change dynamically too, which makes ranking on graphs a challenging work. To address this challenge, we propose the LBSNRank algorithm on graphs with nodes whose contents change dynamically. To validate our algorithm on real datasets, we have crawled and analyzed a dataset from the Dianping website. Experiments on this real dataset show that our LBSNRank algorithm performs better
User-Location Graph GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory
"... People travel in the real world and leave their location history in a form of trajectories. These trajectories do not only connect locations in the physical world but also bridge the gap between people and locations. This paper introduces a social networking service, called GeoLife, which aims to un ..."
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People travel in the real world and leave their location history in a form of trajectories. These trajectories do not only connect locations in the physical world but also bridge the gap between people and locations. This paper introduces a social networking service, called GeoLife, which aims to understand trajectories, locations and users, and mine the correlation between users and locations in terms of user-generated GPS trajectories. GeoLife offers three key applications scenarios: 1) sharing life experiences based on GPS trajectories; 2) generic travel recommendations, e.g., the top interesting locations, travel sequences among locations and travel experts in a given region; and 3) personalized friend and location recommendation. 1.
Specialization
, 2012
"... I firstly express my deepest thanks to my supervisors, Professor Daqing Zhang and Professor Mounir Mokhtari, who gave me the opportunity to do this thesis, and have been providing the help, suggestions and encouragement in all the time during the thesis. I will also thank Professor Shijian Li, who p ..."
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I firstly express my deepest thanks to my supervisors, Professor Daqing Zhang and Professor Mounir Mokhtari, who gave me the opportunity to do this thesis, and have been providing the help, suggestions and encouragement in all the time during the thesis. I will also thank Professor Shijian Li, who provided the taxi GPS dataset and all the testers in my experiment, thank you for the help. My dearest family is the greatest support during my research in these years. Although my
