Abstract
Addressing the challenge of matching queries with the right experts amid temporal-textual inconsistencies, we present a novel approach that combines an attention-based text embedding model with a continuous-time module. This method effectively maps queries to relevant experts by analyzing concept-oriented vectors and user behavior, demonstrating significant effectiveness on StackOverflow and Yahoo datasets.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2024 IEEE 40th International Conference on Data Engineering, ICDE 2024 |
| Publisher | IEEE Computer Society |
| Pages | 5715-5716 |
| Number of pages | 2 |
| ISBN (Electronic) | 9798350317152 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 40th IEEE International Conference on Data Engineering, ICDE 2024 - Utrecht, Netherlands Duration: 13 May 2024 → 17 May 2024 |
Publication series
| Name | Proceedings - International Conference on Data Engineering |
|---|---|
| ISSN (Print) | 1084-4627 |
| ISSN (Electronic) | 2375-0286 |
Conference
| Conference | 40th IEEE International Conference on Data Engineering, ICDE 2024 |
|---|---|
| Country/Territory | Netherlands |
| City | Utrecht |
| Period | 13/05/24 → 17/05/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- context-wise transformers
- online expert recommendation
- time-aware embedding
- user behavioral patterns
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