11 citations · 20 across the 8 of their papers we have counts for
6 papers · 1 filter
Memento: Personalized RAG-Style Long-Retention Data Scaling for META Ads Recommendation
Xiaoyu Chen, Ruichen Wang, Jieming Di +21
Modeling of long history data suffers from long-context window attention dilution, system efficiency and catastrophic forgetting problems, where naive linear scaling approach like…
Intelligent Elastic Feature Fading: Enabling Model Retrain-Free Feature Efficiency Rollouts at Scale
Jieming Di, Xiaoyu Chen, Ying She +21
Large-scale ranking systems depend on thousands of features derived from user behavior across multiple time horizons. Typically requires model retraining -- resulting in long itera…
LLaTTE: Scaling Laws for Multi-Stage Sequence Modeling in Large-Scale Ads Recommendation
Lee Xiong, Zhirong Chen, Rahul Mayuranath +17
We present LLaTTE (LLM-Style Latent Transformers for Temporal Events), a scalable transformer architecture for production ads recommendation. Through systematic experiments, we dem…
ORBIT -- Open Recommendation Benchmark for Reproducible Research with Hidden Tests
Jingyuan He, Jiongnan Liu, Vishan Vishesh Oberoi +7
Recommender systems are among the most impactful AI applications, interacting with billions of users every day, guiding them to relevant products, services, or information tailored…
ClueWeb22: 10 Billion Web Documents with Visual and Semantic Information
Arnold Overwijk, Chenyan Xiong, Xiao Liu +2
ClueWeb22, the newest iteration of the ClueWeb line of datasets, provides 10 billion web pages affiliated with rich information. Its design was influenced by the need for a high qu…
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
Lee Xiong, Chenyan Xiong, Ye Li +5
Conducting text retrieval in a dense learned representation space has many intriguing advantages over sparse retrieval. Yet the effectiveness of dense retrieval (DR) often requires…