Zulong Chen
Biographic Data
| ID | 10055575 |
|---|---|
| NAME | Zulong Chen |
| GIVEN NAMES | Zulong |
| FAMILY NAME | Chen |
| SIGNATURE | CHEN Z |
| AFFILIATIONS | Alibaba Group (China) |
| ORCID | 0000-0003-2354-2761 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Multitask-Based Self-Supervised Learning for Recommendation in Social Systems
In computational social systems, recommendation functionality plays a pivotal role in influencing user behavior, enhancing user experience, and driving engagement. To help recommendation functionality to better suggest relevant content or items, the social platforms usually utilize large-scale knowledge discovery techniques to analyze trends in user interactions and extract patterns from large datasets. Click-through rate (CTR) prediction is cruc…
Exploiting User Preferences for Multiscenarios in Query-Less Search
Online travel platforms (OTPs), for example, booking.com, Ctrip.com, and Fliggy, deliver travel experiences to online users by providing travel-related products. Hotel recommendation is significantly important for OTPs since hotel bookings account for almost half of the travel expenses and hotel products generate more than half of OTP’s revenues. More than 58% Fliggy users may choose to use query-less hotel searches to find candidate hotels, wher…
No prominent works on this page.
Exploiting User Preferences for Multiscenarios in Query-Less Search
Online travel platforms (OTPs), for example, booking.com, Ctrip.com, and Fliggy, deliver travel experiences to online users by providing travel-related products. Hotel recommendation is significantly important for OTPs since hotel bookings account for almost half of the travel expenses and hotel products generate more than half of OTP’s revenues. More than 58% Fliggy users may choose to use query-less hotel searches to find candidate hotels, wher…
Multitask-Based Self-Supervised Learning for Recommendation in Social Systems
In computational social systems, recommendation functionality plays a pivotal role in influencing user behavior, enhancing user experience, and driving engagement. To help recommendation functionality to better suggest relevant content or items, the social platforms usually utilize large-scale knowledge discovery techniques to analyze trends in user interactions and extract patterns from large datasets. Click-through rate (CTR) prediction is cruc…
Computer Science (2 works) · Recommender Systems and Techniques (2 works) · Advanced Text Analysis Techniques (1 work) · Artificial Intelligence (1 work) · Business (1 work) · Click-through rate (1 work) · Computer security (1 work) · Digital Marketing and Social Media (1 work) · Geography (1 work) · Information retrieval (1 work)