Feng Li

Orcid: 0009-0001-0770-2107

Affiliations:
  • Alibaba Group, Beijing, China


According to our database1, Feng Li authored at least 10 papers between 2019 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
NEZHA: A Zero-sacrifice and Hyperspeed Decoding Architecture for Generative Recommendations.
Proceedings of the ACM Web Conference 2026, 2026

VALUE: Value-Aware Large Language Model for Query Rewriting via Weighted Trie in Sponsored Search.
Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, 2026

LoFT-LLM: Low-Frequency Time-series Forecasting with Large Language Models.
Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, 2026

2025
GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks.
CoRR, June, 2025

VALUE: Value-Aware Large Language Model for Query Rewriting via Weighted Trie in Sponsored Search.
CoRR, April, 2025

2022
APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction.
CoRR, 2022

Joint Optimization of Ad Ranking and Creative Selection.
Proceedings of the SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11, 2022

APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction.
Proceedings of the SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021

2019
Graph Intention Network for Click-through Rate Prediction in Sponsored Search.
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019


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