Work

Joint Promotion Partner Recommendation Systems Using Data from Location-Based Social Networks

Year: 2021

Type: article

Abstract: 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.

Source: ISPRS International Journal of Geo-Information

DOI: 10.3390/ijgi10020057

Authors: Yi-Chung Chen, Hsi-Ho Huang, Sheng-Min Chiu, Chiang Lee

Institutions: National Yunlin University of Science and Technology, National Cheng Kung University

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,512

Funder: Ministry of Science and Technology, Taiwan