Name
Abstract | ||
|---|---|---|
Point-of-interest (POI) recommendation has become more and more important, since it could discover user behavior pattern and find interesting venues for them. To address this problem, we propose a rank-based method, PGRank, which integrates user geographical preference and latent preference into Bayesian personalized ranking framework. The experimental results on a real dataset show its effective.
|
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2872518.2889378 | WWW '16: 25th International World Wide Web Conference
Montréal
Québec
Canada
April, 2016 |
Field | DocType | ISBN |
Data mining,Behavioral pattern,World Wide Web,Information retrieval,Ranking,Computer science,Point of interest,Bayesian probability | Conference | 978-1-4503-4144-8 |
Citations | PageRank | References |
2 | 0.36 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haochao Ying | 1 | 73 | 10.03 |
| Liang Chen | 2 | 258 | 28.02 |
| Yuwen Xiong | 3 | 187 | 8.44 |
| Jian Wu | 4 | 933 | 95.62 |