Gridding-based tensor decomposition algorithm for points of interest prediction in LBSNs

Haoru Du

doi:10.1117/12.2657415

13 January 2023 Gridding-based tensor decomposition algorithm for points of interest prediction in LBSNs

Haoru Du

Proceedings Volume 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022); 125101L (2023) https://doi.org/10.1117/12.2657415
Event: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 2022, Qingdao, China

Abstract

Location based social network (LBSN) provides large amounts of data which record the locations visited by users and corresponding latitude and longitude of these locations. Such datasets can be used to explore visiting preferences of users and predict the locations which are likely to be visited by a particular user in the future. Thus, the problem of prediction of users’ preference locations has become a research hotspot and attracts great attention from academia and practitioners. However, it is still a challenge to precisely predict which locations will be visited by users. The main reason is that the visiting decisions made by users will be affected by not only preferences but also geographical factors. In this paper, we investigate the influence of geographical factors, and propose a gridding-based tensor decomposition algorithm for users’ preference locations prediction. We divide the entire city into grids and fill these grids with visiting records of users. A tensor is constructed according these grids, and a tensor decomposition algorithm is employed to calculate the visiting probability of each grid for each user. Then, we calculate the popularity of locations in each grid. Finally, we construct a ranking list of all locations by considering both grids' visiting probabilities and corresponding popularity scores. We have implemented our algorithm and compared with existing approaches by using two public datasets, Foursquare and Gowalla. The experimental results show that our algorithm achieves higher precision and recall.

Citation Download Citation

Haoru Du "Gridding-based tensor decomposition algorithm for points of interest prediction in LBSNs", Proc. SPIE 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 125101L (13 January 2023); https://doi.org/10.1117/12.2657415

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Social networks

Filtering (signal processing)

Integrated modeling

Matrices

Mining

Mobile communications

Video

Keywords/Phrases

Search In:

Publication Years