Location-based and preference-aware recommendation using sparse geo-social networking data

Jie Bao, Yu Zheng, Mohamed Mokbel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

393 Citations (Scopus)

Abstract

The popularity of location-based social networks provide us with a new platform to understand users' preferences based on their location 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 automatically learned from her location history and 2) Social opinions, which are mined from the location histories of the local experts. 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 traditional collaborative filtering-based location recommender systems. The problem becomes even more challenging when people travel to a new city. To this end, we propose a novel location recommender system, which consists of two main parts: offline modeling and online recommendation. The offline modeling part models each individual's personal preferences with a weighted category hierarchy (WCH) and infers the expertise of each user in a city with respect to different category of locations according to their location histories using an iterative learning model. The online recommendation part selects candidate local experts in a geospatial range that matches the user's preferences using a preference-aware candidate selection algorithm and then infers a score of the candidate locations based on the opinions of the selected local experts. Finally, the top-k ranked locations are returned as the recommendations for the user. We evaluated our system with a large-scale real dataset collected from Foursquare. The results confirm that our method offers more effective recommendations than baselines, while having a good efficiency of providing location recommendations.

Original languageEnglish
Title of host publication20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012
Pages199-208
Number of pages10
DOIs
Publication statusPublished - 1 Dec 2012
Externally publishedYes
Event20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012 - Redondo Beach, CA, United States
Duration: 6 Nov 20129 Nov 2012

Other

Other20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012
CountryUnited States
CityRedondo Beach, CA
Period6/11/129/11/12

Fingerprint

Social Networking
networking
Recommendations
history
Recommender Systems
Recommender systems
User Preferences
social network
modeling
learning
recommendation
matrix
city
Collaborative filtering
Collaborative Filtering
Expertise
Modeling
Range of data
Social Networks
Baseline

Keywords

  • location-based services
  • location-based social networks
  • recommender systems
  • user preferences

ASJC Scopus subject areas

  • Earth-Surface Processes
  • Computer Science Applications
  • Modelling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

Cite this

Bao, J., Zheng, Y., & Mokbel, M. (2012). Location-based and preference-aware recommendation using sparse geo-social networking data. In 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012 (pp. 199-208) https://doi.org/10.1145/2424321.2424348

Location-based and preference-aware recommendation using sparse geo-social networking data. / Bao, Jie; Zheng, Yu; Mokbel, Mohamed.

20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012. 2012. p. 199-208.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Bao, J, Zheng, Y & Mokbel, M 2012, Location-based and preference-aware recommendation using sparse geo-social networking data. in 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012. pp. 199-208, 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012, Redondo Beach, CA, United States, 6/11/12. https://doi.org/10.1145/2424321.2424348
Bao J, Zheng Y, Mokbel M. Location-based and preference-aware recommendation using sparse geo-social networking data. In 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012. 2012. p. 199-208 https://doi.org/10.1145/2424321.2424348
Bao, Jie ; Zheng, Yu ; Mokbel, Mohamed. / Location-based and preference-aware recommendation using sparse geo-social networking data. 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012. 2012. pp. 199-208
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