PACELA: A neural framework for user visitation in location-based social networks
Check-in prediction using location-based social network data is an important research problem for both academia and industry since an accurate check-in predictive model is useful to many applications, e.g. urban planning, venue recommendation, route suggestion, and context-aware advertising. Intuiti...
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sg-smu-ink.sis_research-50832019-06-18T14:04:02Z PACELA: A neural framework for user visitation in location-based social networks DOAN, Thanh Nam LIM, Ee-peng Check-in prediction using location-based social network data is an important research problem for both academia and industry since an accurate check-in predictive model is useful to many applications, e.g. urban planning, venue recommendation, route suggestion, and context-aware advertising. Intuitively, when considering venues to visit, users may rely on their past observed visit histories as well as some latent attributes associated with the venues. In this paper, we therefore propose a check-in prediction model based on a neural framework called Preference and Context Embeddings with Latent Attributes (PACELA). PACELA learns the embeddings space for the user and venue data as well as the latent attributes of both users and venues. More specifically, we use a probabilistic matrix factorization-based technique to infer the latent attributes specific to users and locations in location-based social networks (LBSNs), considering the user visitation decisions that could be affected by area attraction, neighborhood competition, and social homophily. PACELA also includes a deep learning neural network to combine both embedding and latent features to predict if a user performs check-in on a location. Our experiments on three different real world datasets show that PACELA yields the best check-in prediction accuracy against several baseline methods. 2018-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4080 info:doi/10.1145/3209219.3209231 https://ink.library.smu.edu.sg/context/sis_research/article/5083/viewcontent/p13_doan.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 Neural Network Check-in Prediction Location-based social networks User visitation Databases and Information Systems Social Media |
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Neural Network Check-in Prediction Location-based social networks User visitation Databases and Information Systems Social Media DOAN, Thanh Nam LIM, Ee-peng PACELA: A neural framework for user visitation in location-based social networks |
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Check-in prediction using location-based social network data is an important research problem for both academia and industry since an accurate check-in predictive model is useful to many applications, e.g. urban planning, venue recommendation, route suggestion, and context-aware advertising. Intuitively, when considering venues to visit, users may rely on their past observed visit histories as well as some latent attributes associated with the venues. In this paper, we therefore propose a check-in prediction model based on a neural framework called Preference and Context Embeddings with Latent Attributes (PACELA). PACELA learns the embeddings space for the user and venue data as well as the latent attributes of both users and venues. More specifically, we use a probabilistic matrix factorization-based technique to infer the latent attributes specific to users and locations in location-based social networks (LBSNs), considering the user visitation decisions that could be affected by area attraction, neighborhood competition, and social homophily. PACELA also includes a deep learning neural network to combine both embedding and latent features to predict if a user performs check-in on a location. Our experiments on three different real world datasets show that PACELA yields the best check-in prediction accuracy against several baseline methods. |
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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 |
PACELA: A neural framework for user visitation in location-based social networks |
title_short |
PACELA: A neural framework for user visitation in location-based social networks |
title_full |
PACELA: A neural framework for user visitation in location-based social networks |
title_fullStr |
PACELA: A neural framework for user visitation in location-based social networks |
title_full_unstemmed |
PACELA: A neural framework for user visitation in location-based social networks |
title_sort |
pacela: a neural framework for user visitation in location-based social networks |
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Institutional Knowledge at Singapore Management University |
publishDate |
2018 |
url |
https://ink.library.smu.edu.sg/sis_research/4080 https://ink.library.smu.edu.sg/context/sis_research/article/5083/viewcontent/p13_doan.pdf |
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