Liangmin Guo
Biographic Data
| ID | 10053901 |
|---|---|
| NAME | Liangmin Guo |
| GIVEN NAMES | Liangmin |
| FAMILY NAME | Guo |
| SIGNATURE | GUO L |
| AFFILIATIONS | Anhui Normal University |
| ORCID | 0000-0003-3402-6391 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Next Point of Interest Recommendation Based on Graph Structure and Sequential Pattern
In location-based social networks, next point of interest (POI) recommendation predicts the next-visited POIs of users by mining their behavioral patterns. However, existing POI recommendation methods based on graph neural networks and attention mechanisms fail to adequately capture: 1) the local structural features influenced by the trajectories of other users (i.e., the relationships between POIs visited by users); and 2) the dynamic visitation…
Knowledge Graph-Based Personalized Multitask Enhanced Recommendation
To address the problem of data sparsity in recommendation systems, various studies have used knowledge graphs as auxiliary information. These studies have employed multitask learning (MTL) to enhance recommendation performance. However, the shared information between tasks is not fully explored when using an MTL strategy for training both recommendation and knowledge graph-related tasks. Moreover, most studies cannot effectively model the knowled…
A Matrix Factorization Recommendation System-Based Local Differential Privacy for Protecting Users’ Sensitive Data
The recommendation system (RS) predicts user ratings by collecting user information, but the users’ private information may be exposed in this process. Thus, it is crucial to achieving a balance between recommendation performance and privacy-preserving of RSs. Aiming to solve the above problem, this article proposes a novel matrix factorization (MF) algorithm. The algorithm predicts the user rating through a linear weighting of global average rat…
No prominent works on this page.
A Matrix Factorization Recommendation System-Based Local Differential Privacy for Protecting Users’ Sensitive Data
The recommendation system (RS) predicts user ratings by collecting user information, but the users’ private information may be exposed in this process. Thus, it is crucial to achieving a balance between recommendation performance and privacy-preserving of RSs. Aiming to solve the above problem, this article proposes a novel matrix factorization (MF) algorithm. The algorithm predicts the user rating through a linear weighting of global average rat…
Knowledge Graph-Based Personalized Multitask Enhanced Recommendation
To address the problem of data sparsity in recommendation systems, various studies have used knowledge graphs as auxiliary information. These studies have employed multitask learning (MTL) to enhance recommendation performance. However, the shared information between tasks is not fully explored when using an MTL strategy for training both recommendation and knowledge graph-related tasks. Moreover, most studies cannot effectively model the knowled…
Next Point of Interest Recommendation Based on Graph Structure and Sequential Pattern
In location-based social networks, next point of interest (POI) recommendation predicts the next-visited POIs of users by mining their behavioral patterns. However, existing POI recommendation methods based on graph neural networks and attention mechanisms fail to adequately capture: 1) the local structural features influenced by the trajectories of other users (i.e., the relationships between POIs visited by users); and 2) the dynamic visitation…
Recommender Systems and Techniques (3 works) · Computer Science (2 works) · Graph (2 works) · Recommender system (2 works) · Artificial Intelligence (1 work) · Attention network (1 work) · Computer security (1 work) · Convolutional neural network (1 work) · Data mining (1 work) · Differential privacy (1 work)