Alireza Moayedikia
Biographic Data
| ID | 7245374 |
|---|---|
| NAME | Alireza Moayedikia |
| GIVEN NAMES | Alireza |
| FAMILY NAME | Moayedikia |
| SIGNATURE | MOAYEDIKIA A |
| AFFILIATIONS | Swinburne University of Technology |
| ORCID | 0000-0001-7229-3699 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Three-layered location recommendation algorithm using spectral clustering
Users utilize Location-Based Social Networks (LBSNs) to check into diverse venues and share their experiences through ratings and comments. However, these platforms typically feature a considerably larger number of locations than users, resulting in a challenge known as insufficient historical data or user-location matrix sparsity. This sparsity arises because not all users can check into all available locations on a given LBSN, such as Yelp. To …
An integrated crowdshipping framework for green last mile delivery
Deciding discipline, course and university through Topsis
During course selection, students have to consider many criteria to arrive at the best course to study at the institution that provides the best fit for their aspirations. However, a central issue that reduces the clarity of course consideration and results in suboptimal decisions is the failure of introspection. While people can usually describe the information they have considered to arrive at a decision for a specified outcome, they can rarely…
No prominent works on this page.
Deciding discipline, course and university through Topsis
During course selection, students have to consider many criteria to arrive at the best course to study at the institution that provides the best fit for their aspirations. However, a central issue that reduces the clarity of course consideration and results in suboptimal decisions is the failure of introspection. While people can usually describe the information they have considered to arrive at a decision for a specified outcome, they can rarely…
An integrated crowdshipping framework for green last mile delivery
Three-layered location recommendation algorithm using spectral clustering
Users utilize Location-Based Social Networks (LBSNs) to check into diverse venues and share their experiences through ratings and comments. However, these platforms typically feature a considerably larger number of locations than users, resulting in a challenge known as insufficient historical data or user-location matrix sparsity. This sparsity arises because not all users can check into all available locations on a given LBSN, such as Yelp. To …
Computer Science (3 works) · Artificial Intelligence (2 works) · Engineering (2 works) · Human Mobility and Location-Based Analysis (2 works) · Operations research (2 works) · Algorithm (1 works) · Analytics (1 works) · Business (1 works) · CLARITY (1 works) · Cluster analysis (1 works)