Modeling location-based social network data with area attraction and neighborhood competition-Reference-Cited by-同舟云学术

Modeling location-based social network data with area attraction and neighborhood competition

Published:2018-09-18 Issue:1 Volume:33 Page:58-95
ISSN:1384-5810
Container-title:Data Mining and Knowledge Discovery
language:en
Short-container-title:Data Min Knowl Disc

Author:

Doan Thanh-Nam^ORCID,Lim Ee-Peng

Funder

National Research Foundation Singapore

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://link.springer.com/article/10.1007/s10618-018-0588-4/fulltext.html

Reference54 articles.

1. Backstrom L, Sun E, Marlow C (2010) Find me if you can: improving geographical prediction with social and spatial proximity. In: The 19th international conference on World Wide Web (WWW). ACM, New York, pp 61–70

2. Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3(Jan):993–1022

3. Blei DM, Jordan MI (2006) Variational inference for Dirichlet process mixtures. Bayesian Anal 1(1):121–143

4. Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, New York

5. Chang J, Sun E (2011) Location 3: how users share and respond to location-based data on social networking sites. In: 5th international AAAI conference on weblogs and social media (ICWSM), pp 74–80

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Identification of Land Use Mix Using Point-Based Geospatial Data in Urban Areas;Applied Sciences;2024-08-06

2. In Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data (Vision Paper);ACM Transactions on Spatial Algorithms and Systems;2024-06-30

3. Bias characterization, assessment, and mitigation in location-based recommender systems;Data Mining and Knowledge Discovery;2023-02-14

4. Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches;Electronics;2022-06-26

5. DeePOF: A hybrid approach of deep convolutional neural network and friendship to Point‐of‐Interest (POI) recommendation system in location‐based social networks;Concurrency and Computation: Practice and Experience;2022-04-15