Joint Promotion Partner Recommendation Systems Using Data from Location-Based Social Networks
Bibliographic Data
| ID | 22033443 |
|---|---|
| Authors | Yi-Chung Chen (0000-0003-0353-7340, National Yunlin University of Science and Technology, corresponding author), Hsi-Ho Huang (National Cheng Kung University), Sheng-Min Chiu (0000-0002-6962-866X, National Cheng Kung University), Chiang Lee (0000-0003-2531-2150, National Cheng Kung University) |
| Year | 2021 |
| Volume | 10 |
| Issue | 2 |
| Pages | 57 |
| Publication date | 2021-01-30 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10020057 |
| OpenAlex | W3128815041 |
| Language | EN |
| Citations received | 3 |
| References cited | 68 |
Joint promotion is a valuable business strategy that enables companies to attract more customers at lower operational cost. However, finding a suitable partner can be extremely difficult. Conventionally, one of the most common approaches is to conduct survey-based analysis; however, this method can be unreliable as well as time-consuming, considering that there are likely to be thousands of potential partners in a city. This article proposes a framework to recommend Joint Promotion Partners using location-based social networks (LBSN) data. We considered six factors in determining the suitability of a partner (customer base, association, rating and awareness, prices and star ratings, distance, and promotional strategy) and developed efficient algorithms to perform the required calculations. The effectiveness and efficiency of our algorithms were verified using the Foursquare dataset and real-life case studies
Data mining · Data science · Operations research · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Transportation Planning and Optimization · Urban and Freight Transport Logistics
Applying Check-in Data and User Profiles to Identify Optimal Store Locations in a Road Network
A Smart Tourism Recommendation Algorithm Based on Cellular Geospatial Clustering and Multivariate Weighted Collaborative Filtering
Categorisation of cultural tourism attractions by tourist preference using location-based social network data
| Citations per year | 0.6 |
|---|---|
| Citation span | 2021 - 2022 (2) |
| Highly cited | No |