CityVoyager: An Outdoor Recommendation System Based on User Location History

Takeuchi, Yuichiro; Sugimoto, Masanori

doi:10.1007/11833529_64

Cited by 126 publications

(65 citation statements)

References 12 publications

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“…In contrast to these techniques, we aim to integrate social networking into the mobile tourist guide systems, by helping each individual deeply understand the locations around them with the knowledge mined from multiple users' location histories. Recommenders based on location history: Using multiple users' real-world location histories, some recommender systems, such as Geowhiz [10] and CityVoyager [18], etc, have been designed to recommend geographic locations like shops or restaurants to users. Horozov et al [10] proposed an enhanced collaborative filtering solution to generate the recommendation of a restaurant.…”

Section: Discussion On Classical Sequencesmentioning

confidence: 99%

“…Horozov et al [10] proposed an enhanced collaborative filtering solution to generate the recommendation of a restaurant. Takeuchi et al [18] attempted to recommend shops to users based on their individual preferences estimated by analyzing their past location histories. The major difference between these works and ours lies in two aspects.…”

Section: Discussion On Classical Sequencesmentioning

confidence: 99%

See 1 more Smart Citation

Mining interesting locations and travel sequences from GPS trajectories

Zheng

Zhang

Xie

et al. 2009

Proceedings of the 18th International Conference on World Wide Web

1,602

904

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The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people's location histories. In this paper, based on multiple users' GPS trajectories, we aim to mine interesting locations and classical 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, etc. Such information can help users understand surrounding locations, and would enable travel recommendation. In this work, we first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG). Second, 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 the interest of a location by taking into account the following three factors. 1) The interest of a location depends on not only the number of users visiting this location but also these users' travel experiences. 2) Users' travel experiences and location interests have a mutual reinforcement relationship.3) The interest of a location and the travel experience of a user are relative values and are region-related. Third, we mine the classical travel sequences among locations considering the interests of these locations and users' travel experiences. We evaluated our system using a large GPS dataset collected by 107 users over a period of one year in the real world. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, when considering the users' travel experiences and location interests, we achieved a better performance beyond baselines, such as rankby-count and rank-by-interest, etc.

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Section: Discussion On Classical Sequencesmentioning

confidence: 99%

Section: Discussion On Classical Sequencesmentioning

confidence: 99%

Mining interesting locations and travel sequences from GPS trajectories

Zheng

Zhang

Xie

et al. 2009

Proceedings of the 18th International Conference on World Wide Web

1,602

904

View full text Add to dashboard Cite

show abstract

“…Location-aware recommenders. The CityVoyager system [28] mines a user's personal GPS trajectory data to determine her preferred shopping sites, and provides recommendation based on where the system predicts the user is likely to go in the future. LARS, conversely, does not attempt to predict future user movement, as it produces recommendations influenced by user and/or item locations embedded in community ratings.…”

Section: Related Workmentioning

confidence: 99%

LARS: A Location-Aware Recommender System

Levandoski

Sarwat

Eldawy

et al. 2012

2012 IEEE 28th International Conference on Data Engineering

332

183

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Abstract-This paper proposes LARS, a location-aware recommender system that uses location-based ratings to produce recommendations. Traditional recommender systems do not consider spatial properties of users nor items; LARS, on the other hand, supports a taxonomy of three novel classes of locationbased ratings, namely, spatial ratings for non-spatial items, nonspatial ratings for spatial items, and spatial ratings for spatial items. LARS exploits user rating locations through user partitioning, a technique that influences recommendations with ratings spatially close to querying users in a manner that maximizes system scalability while not sacrificing recommendation quality. LARS exploits item locations using travel penalty, a technique that favors recommendation candidates closer in travel distance to querying users in a way that avoids exhaustive access to all spatial items. LARS can apply these techniques separately, or together, depending on the type of location-based rating available. Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the MovieLens movie recommendation system reveals that LARS is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.

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“…With research showing that users are increasingly willing to share their location in return for services, these applications can provide geo-social recommendations about people, places and events of interests anytime, anywhere. The first steps in this direction have already been taken by a number of context-aware recommendation systems (Espinoza et al, 2001;Heijden et al, 2005;Takeuchi and Sugimoto, 2005;Yang et al, 2008). While these systems consider location or user preferences when making recommendations, they do not take into account group membership and associations between groups and places.…”

Section: Introductionmentioning

confidence: 99%

Automatic identification of informal social groups and places for geo-social recommendations

Gupta

Paul

Jones

et al. 2007

IJMNDI

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Mobile locatable devices can help identify previously unknown ad hoc or semi-permanent groups of people and their meeting places. Newly identified groups or places can be recommended to people to enhance their geo-social experience, while respecting privacy constraints. For instance, new students can learn about popular hangouts on campus or faculty members can learn about groups of students routinely having research discussions. This paper presents a clustering algorithm based on user copresence that identifies such groups and places even when group members participate to only a certain fraction of meetings. Simulation results demonstrate that 90-96% of group members can be identified with negligible false positives when the user meeting attendance is at least 50%. Experimental results using one-month of mobility traces collected from smart phones running Intel's PlaceLab location engine successfully identified all groups that met regularly during that period. Additionally, the group places were identified with good accuracy.Keywords: mobile social computing; location aware recommender systems; group identification; place identification.Reference to this paper should be made as follows: Gupta, A., Paul, S., Jones, Q., and Quentin Jones is an Assistant Professor at NJIT. He is the Director of NJIT's SmartCampus Project, an effort to explore location-aware community system design, utility and social impacts. His research and teaching focus is social computing with an emphasis on the design of collaborative environments. He has a PhD in

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CityVoyager: An Outdoor Recommendation System Based on User Location History

Cited by 126 publications

References 12 publications

Mining interesting locations and travel sequences from GPS trajectories

Mining interesting locations and travel sequences from GPS trajectories

LARS: A Location-Aware Recommender System

Automatic identification of informal social groups and places for geo-social recommendations

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