Digest: Unified Low-rank Compression Framework for Click-through Rate Prediction
KDD 2024 / Recommender Systems
Unified Low-rank Compression Framework for Click-through Rate Prediction
A CTR prediction paper on low-rank compression for reducing model cost while preserving practical ranking behavior.
Research area
User reactivation, generative recommendation, dynamic retrieval, graph embedding, counterfactual watch-time prediction, and efficient CTR modeling form the main technical line.
Publication record
- Authors
- H Yu, M Fu, J Ding, Y Zhou, J Wu
- Venue
- Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
- Year
- 2024
- Area
- Recommender Systems