Next Article in Journal
A Conjecture on the Nature of Information, with a “Simple” Example
Previous Article in Journal
A Frequency-Based Assignment Model under Day-to-Day Information Evolution of Oversaturated Conditions on a Feeder Bus Service
Article Menu

Export Article

Open AccessArticle
Information 2017, 8(1), 20; doi:10.3390/info8010020

Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs

1
School of Management Science and Engineering, Shandong Normal University, Jinan 250014, China
2
Information Technology Bureau of Shandong Province, China Post Group, Jinan 250001, China
3
School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China
*
Author to whom correspondence should be addressed.
Academic Editor: Kurt Maly
Received: 12 December 2016 / Revised: 23 January 2017 / Accepted: 26 January 2017 / Published: 6 February 2017
View Full-Text   |   Download PDF [448 KB, uploaded 6 February 2017]   |  

Abstract

Point-of-interest (POI) recommendation has been well studied in recent years. However, most of the existing methods focus on the recommendation scenarios where users can provide explicit feedback. In most cases, however, the feedback is not explicit, but implicit. For example, we can only get a user’s check-in behaviors from the history of what POIs she/he has visited, but never know how much she/he likes and why she/he does not like them. Recently, some researchers have noticed this problem and began to learn the user preferences from the partial order of POIs. However, these works give equal weight to each POI pair and cannot distinguish the contributions from different POI pairs. Intuitively, for the two POIs in a POI pair, the larger the frequency difference of being visited and the farther the geographical distance between them, the higher the contribution of this POI pair to the ranking function. Based on the above observations, we propose a weighted ranking method for POI recommendation. Specifically, we first introduce a Bayesian personalized ranking criterion designed for implicit feedback to POI recommendation. To fully utilize the partial order of POIs, we then treat the cost function in a weighted way, that is give each POI pair a different weight according to their frequency of being visited and the geographical distance between them. Data analysis and experimental results on two real-world datasets demonstrate the existence of user preference on different POI pairs and the effectiveness of our weighted ranking method. View Full-Text
Keywords: point-of-interest; location recommendation; LBSNs point-of-interest; location recommendation; LBSNs
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Guo, L.; Jiang, H.; Wang, X.; Liu, F. Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs. Information 2017, 8, 20.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top