Ayong Ye
Biographic Data
| ID | 10054152 |
|---|---|
| NAME | Ayong Ye |
| GIVEN NAMES | Ayong |
| FAMILY NAME | Ye |
| SIGNATURE | YE A |
| AFFILIATIONS | Fujian Normal University |
| ORCID | 0000-0002-2606-5406 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Large Language Model Embedding for Cold-Start Item Recommendation via Data Augmentation and Regularization
The cold-start problem constitutes a persistent challenge in recommender systems (RecSys). Recent advances in large language model (LLM) for natural language processing have inspired researchers to utilize their semantic understanding and knowledge extraction capabilities in recommendation tasks. LLM shows clear advantages in addressing cold-start items. However, current methods often rely on direct online LLM inference. This approach causes high…
Service-Splitting-Based Privacy Protection Mechanism for Proximity Detection Supporting High Utility
Proximity detection is one of the most popular location-based applications in social networks when users intend to find their nearby friends. However, the existing proximity detection has access to precise and real-time location information of users, raising serious privacy concerns for millions of users. A number of privacy-preserving models have taken shape over the past decade, but they almost universally rely on syntactic privacy models such …
No prominent works on this page.
Service-Splitting-Based Privacy Protection Mechanism for Proximity Detection Supporting High Utility
Proximity detection is one of the most popular location-based applications in social networks when users intend to find their nearby friends. However, the existing proximity detection has access to precise and real-time location information of users, raising serious privacy concerns for millions of users. A number of privacy-preserving models have taken shape over the past decade, but they almost universally rely on syntactic privacy models such …
Large Language Model Embedding for Cold-Start Item Recommendation via Data Augmentation and Regularization
The cold-start problem constitutes a persistent challenge in recommender systems (RecSys). Recent advances in large language model (LLM) for natural language processing have inspired researchers to utilize their semantic understanding and knowledge extraction capabilities in recommendation tasks. LLM shows clear advantages in addressing cold-start items. However, current methods often rely on direct online LLM inference. This approach causes high…
Artificial Intelligence (1 work) · Business (1 work) · Computer Science (1 work) · Computer security (1 work) · Cryptography and Data Security (1 work) · Data modeling (1 work) · Deep learning (1 work) · Embedding (1 work) · Information privacy (1 work) · Internet privacy (1 work)