Xun Su
Biographic Data
| ID | 7998958 |
|---|---|
| NAME | Xun Su |
| GIVEN NAMES | Xun |
| FAMILY NAME | Su |
| SIGNATURE | SU X |
| AFFILIATIONS | Renmin University of China |
| ORCID | 0009-0005-8287-0732 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Modeling Multi-Grained User Interests for Sequential Recommendation
Sequential recommendation has garnered considerable attention in recent years, which aims to infer the next desired item for each user based on their historical behavior data. Despite the remarkable progress made by previous methods, we believe that they still have two limitations: 1) they mainly focus on extracting pairwise transition pattern between two items within each individual sequence, how to fully capture the collective influence of a gr…
Maize migration mitigates the negative impact of climate change on China’s maize yield
Crop migration as an adaptation to modulate climate change’s impact on crop yields presents both benefits and risks. We explored how maize migration in China modulates yield responses to climate change and quantified the potential economic benefits of maize migration as an adaptation strategy. We employed a panel data model to identify and measure the factors driving the relocation of maize area, linear regression to quantify the effects of maize…
No prominent works on this page.
Maize migration mitigates the negative impact of climate change on China’s maize yield
Crop migration as an adaptation to modulate climate change’s impact on crop yields presents both benefits and risks. We explored how maize migration in China modulates yield responses to climate change and quantified the potential economic benefits of maize migration as an adaptation strategy. We employed a panel data model to identify and measure the factors driving the relocation of maize area, linear regression to quantify the effects of maize…
Modeling Multi-Grained User Interests for Sequential Recommendation
Sequential recommendation has garnered considerable attention in recent years, which aims to infer the next desired item for each user based on their historical behavior data. Despite the remarkable progress made by previous methods, we believe that they still have two limitations: 1) they mainly focus on extracting pairwise transition pattern between two items within each individual sequence, how to fully capture the collective influence of a gr…
Advanced Bandit Algorithms Research (1 work) · Agriculture (1 work) · Agriculture Sustainability and Environmental Impact (1 work) · Agronomy (1 work) · Biology (1 work) · Climate change (1 work) · Climate change impacts on agriculture (1 work) · Climate Change, Adaptation, Migration (1 work) · Crop simulation model (1 work) · Crop yield (1 work)