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65
Collaborative location and activity recommendations with gps history data
- In WWW ’10: Proc. of the 19th International World Wide Web Conference
, 2010
"... With the increasing popularity of location-based services, such as tour guide and location-based social network, we now have accumulated many location data on the Web. In this paper, we show that, by using the location data based on GPS and users’ comments at various locations, we can discover inter ..."
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Cited by 93 (9 self)
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With the increasing popularity of location-based services, such as tour guide and location-based social network, we now have accumulated many location data on the Web. In this paper, we show that, by using the location data based on GPS and users’ comments at various locations, we can discover interesting locations and possible activities that can be performed there for recommendations. Our research is highlighted in the following location-related queries in our daily life: 1) if we want to do something such as sightseeing or food-hunting in a large city such as Beijing, where should we go? 2) If we have already visited some places such as the Bird’s Nest building in Beijing’s Olympic park, what else can we do there? By using our system, for the first question, we can recommend her to visit a list of interesting locations such as Tiananmen Square, Bird’s Nest, etc. For the
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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Cited by 68 (15 self)
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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 usergenerated 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
Location-based and preference-aware recommendation using sparse geo-social networking data
- In GIS
, 2012
"... The popularity of location-based social networks provide us with a new platform to understand users ’ preferences based on their lo-cation histories. In this paper, we present a location-based and preference-aware recommender system that offers a particular user a set of venues (such as restaurants) ..."
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Cited by 45 (4 self)
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The popularity of location-based social networks provide us with a new platform to understand users ’ preferences based on their lo-cation histories. In this paper, we present a location-based and preference-aware recommender system that offers a particular user a set of venues (such as restaurants) within a geospatial range with the consideration of both: 1) User preferences, which are auto-matically learned from her location history and 2) Social opin-ions, which are mined from the location histories of the local ex-perts. This recommender system can facilitate people’s travel not only near their living areas but also to a city that is new to them. As a user can only visit a limited number of locations, the user-locations matrix is very sparse, leading to a big challenge to tradi-tional collaborative filtering-based location recommender systems. The problem becomes even more challenging when people travel to
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 44 (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
GeoLife2.0: A LocationBased Social Networking Service
- In Proc. of MDM 2009
"... GeoLife2.0 is a GPS-data-driven social networking service where people can share life experiences and connect to each other with their location histories. By mining people’s location history, GeoLife can measure the similarity between users and perform personalized friend recommendation for an indiv ..."
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Cited by 28 (14 self)
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GeoLife2.0 is a GPS-data-driven social networking service where people can share life experiences and connect to each other with their location histories. By mining people’s location history, GeoLife can measure the similarity between users and perform personalized friend recommendation for an individual. Later, we can predict the individual’s interest level in the locations visited by their friends while have not been found by them. The locations with relatively high interesting level can be recommended. Therefore, GeoLife2.0 can expand a user’s social network, provide them with a trustworthy resource matching their interests and help them sponsor georelated activities like cycling with minimal effort. 1.
Exploring Temporal Effects for Location Recommendation on Location-Based Social Networks
"... Location-based social networks (LBSNs) have attracted an inordinate number of users and greatly enriched the urban experience in recent years. The availability of spatial, temporal and social information in online LBSNs offers an unprecedented opportunity to study various aspects of human behavior, ..."
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Cited by 21 (5 self)
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Location-based social networks (LBSNs) have attracted an inordinate number of users and greatly enriched the urban experience in recent years. The availability of spatial, temporal and social information in online LBSNs offers an unprecedented opportunity to study various aspects of human behavior, and enable a variety of location-based services such as location recommendation. Previous work studied spatial and social influences on location recommendation in LBSNs. Due to the strong correlations between a user’s check-in time and the corresponding check-in location, recommender systems designed for location recommendation inevitably need to consider temporal effects. In this paper, we introduce a novel location recommendation framework, based on the temporal properties of user movement observed from a real-world LBSN dataset. The experimental results exhibit the significance of temporal patterns in explaining user behavior, and demonstrate their power to improve location recommendation performance.
Semantic Trajectory Mining for Location Prediction
- Proceedings of The 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS' 11
, 2011
"... Research on predicting movements of mobile users has attracted a lot of attentions in recent years. Many of those prediction techniques are developed based only on geographic features of mobile users ’ trajectories. In this paper, we propose a novel approach for predicting the next location of a use ..."
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Cited by 20 (2 self)
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Research on predicting movements of mobile users has attracted a lot of attentions in recent years. Many of those prediction techniques are developed based only on geographic features of mobile users ’ trajectories. In this paper, we propose a novel approach for predicting the next location of a user’s movement based on both the geographic and semantic features of users’ trajectories. The core idea of our prediction model is based on a novel cluster-based prediction strategy which evaluates the next location of a mobile user based on the frequent behaviors of similar users in the same cluster determined by analyzing users’ common behavior in semantic trajectories. Through a comprehensive evaluation by experiments, our proposal is shown to deliver excellent performance.
Mining User Similarity from Semantic Trajectories
- In Proceedings of ACM SIGSPATIAL International Workshop on Location Based Social Networks
, 2010
"... In recent years, research on measuring trajectory similarity has attracted a lot of attentions. Most of similarities are defined based on the geographic features of mobile users ’ trajectories. However, trajectories geographically close may not necessarily be similar because the activities implied b ..."
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Cited by 20 (5 self)
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In recent years, research on measuring trajectory similarity has attracted a lot of attentions. Most of similarities are defined based on the geographic features of mobile users ’ trajectories. However, trajectories geographically close may not necessarily be similar because the activities implied by nearby landmarks they pass through may be different. In this paper, we argue that a better similarity measurement should have taken into account the semantics of trajectories. In this paper, we propose a novel approach for recommending potential friends based on users’ semantic trajectories for location-based social networks. The core of our proposal is a novel trajectory similarity measurement, namely, Maximal Semantic Trajectory Pattern Similarity (MSTP-Similarity), which measures the semantic similarity between trajectories. Accordingly, we propose a user similarity measurement based on MSTP-Similarity of user trajectories and use it as the basis for recommending potential friends to a user. Through experimental evaluation, the proposed friend recommendation approach is shown to deliver excellent performance.
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 20 (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].
What you are is when you are: the temporal dimension of feature types in location-based social networks
- In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
, 2011
"... Feature types play a crucial role in understanding and analyzing geographic information. Usually, these types are defined, standardized, and controlled by domain experts and cover geographic features on the mesoscale level, e.g., populated places, forests, or lakes. While feature types also underlie ..."
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Cited by 16 (2 self)
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Feature types play a crucial role in understanding and analyzing geographic information. Usually, these types are defined, standardized, and controlled by domain experts and cover geographic features on the mesoscale level, e.g., populated places, forests, or lakes. While feature types also underlie most Location-Based Services (LBS), assigning a consistent typing schema for Points Of Interest (POI) across different data sets is challenging. In case of Volunteered Geographic Information (VGI), types are assigned as tags by a heterogeneous community with different backgrounds and applications in mind. Consequently, VGI research is shifting away from data completeness and positional accuracy as quality measures towards attribute accuracy. As tags can be assigned by everybody and have no formal or stable definition, we propose to study category tags via indirect observations. We extract user check-ins from massive real-world data crawled from Location-based Social Networks to understand the temporal dimension of Points Of Interest. While users may assign different category tags to places, we argue that their temporal characteristics, e.g., opening times, will
