Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2018
Abstract
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.
Keywords
Neural Network, Check-in Prediction, Location-based social networks, User visitation
Discipline
Databases and Information Systems | Social Media
Research Areas
Data Science and Engineering
Publication
UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, Singapore, July 8-11
First Page
13
Last Page
21
ISBN
9781450355896
Identifier
10.1145/3209219.3209231
Publisher
ACM
City or Country
New York
Citation
DOAN, Thanh Nam and LIM, Ee-peng.
PACELA: A neural framework for user visitation in location-based social networks. (2018). UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, Singapore, July 8-11. 13-21.
Available at: https://ink.library.smu.edu.sg/sis_research/4080
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3209219.3209231