Modeling check-in behavior with geographical neighborhood influence of venues
With many users adopting location-based social networks (LBSNs) to share their daily activities, LBSNs become a gold mine for researchers to study human check-in behavior. Modeling such behavior can benefit many useful applications such as urban planning and location-aware recommender systems. Unlik...
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sg-smu-ink.sis_research-49592020-03-27T02:43:01Z Modeling check-in behavior with geographical neighborhood influence of venues DOAN, Thanh Nam LIM, Ee Peng With many users adopting location-based social networks (LBSNs) to share their daily activities, LBSNs become a gold mine for researchers to study human check-in behavior. Modeling such behavior can benefit many useful applications such as urban planning and location-aware recommender systems. Unlike previous studies [4,6,12,17] that focus on the effect of distance on users checking in venues, we consider two venue-specific effects of geographical neighborhood influence, namely, spatial homophily and neighborhood competition. The former refers to the fact that venues share more common features with their spatial neighbors, while the latter captures the rivalry of a venue and its nearby neighbors in order to gain visitation from users. In this paper, through an extensive empirical study, we show that these two geographical effects, together with social homophily, play significant roles in understanding users’ check-in behaviors. From the observation, we then propose to model users’ check-in behavior by incorporating these effects into a matrix factorization-based framework. To evaluate our proposed models, we conduct check-in prediction task and show that our models outperform the baselines. Furthermore, we discover that neighborhood competition effect has more impact to the users’ check-in behavior than spatial homophily. To the best of our knowledge, this is the first study that quantitatively examine the two effects of geographical neighborhood influence on users’ check-in behavior. 2017-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3957 info:doi/10.1007/978-3-319-69179-4_30 https://ink.library.smu.edu.sg/context/sis_research/article/4959/viewcontent/ModelingCheck_In_Behavior_Geographical_Neighborhood_Influence_Venues_2017.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Common features Competition effects Empirical studies Location-aware Location-based social networks Matrix factorizations Prediction tasks Specific effects Databases and Information Systems Geographic Information Sciences |
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Common features Competition effects Empirical studies Location-aware Location-based social networks Matrix factorizations Prediction tasks Specific effects Databases and Information Systems Geographic Information Sciences DOAN, Thanh Nam LIM, Ee Peng Modeling check-in behavior with geographical neighborhood influence of venues |
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With many users adopting location-based social networks (LBSNs) to share their daily activities, LBSNs become a gold mine for researchers to study human check-in behavior. Modeling such behavior can benefit many useful applications such as urban planning and location-aware recommender systems. Unlike previous studies [4,6,12,17] that focus on the effect of distance on users checking in venues, we consider two venue-specific effects of geographical neighborhood influence, namely, spatial homophily and neighborhood competition. The former refers to the fact that venues share more common features with their spatial neighbors, while the latter captures the rivalry of a venue and its nearby neighbors in order to gain visitation from users. In this paper, through an extensive empirical study, we show that these two geographical effects, together with social homophily, play significant roles in understanding users’ check-in behaviors. From the observation, we then propose to model users’ check-in behavior by incorporating these effects into a matrix factorization-based framework. To evaluate our proposed models, we conduct check-in prediction task and show that our models outperform the baselines. Furthermore, we discover that neighborhood competition effect has more impact to the users’ check-in behavior than spatial homophily. To the best of our knowledge, this is the first study that quantitatively examine the two effects of geographical neighborhood influence on users’ check-in behavior. |
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DOAN, Thanh Nam LIM, Ee Peng |
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DOAN, Thanh Nam LIM, Ee Peng |
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DOAN, Thanh Nam |
title |
Modeling check-in behavior with geographical neighborhood influence of venues |
title_short |
Modeling check-in behavior with geographical neighborhood influence of venues |
title_full |
Modeling check-in behavior with geographical neighborhood influence of venues |
title_fullStr |
Modeling check-in behavior with geographical neighborhood influence of venues |
title_full_unstemmed |
Modeling check-in behavior with geographical neighborhood influence of venues |
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modeling check-in behavior with geographical neighborhood influence of venues |
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Institutional Knowledge at Singapore Management University |
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2017 |
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https://ink.library.smu.edu.sg/sis_research/3957 https://ink.library.smu.edu.sg/context/sis_research/article/4959/viewcontent/ModelingCheck_In_Behavior_Geographical_Neighborhood_Influence_Venues_2017.pdf |
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