63 citations · 91 across the 8 of their papers we have counts for
8 papers
An Efficient Sparse Inference Software Accelerator for Transformer-based Language Models on CPUs
Haihao Shen, Hengyu Meng, Bo Dong +9
In recent years, Transformer-based language models have become the standard approach for natural language processing tasks. However, stringent throughput and latency requirements i…
Fast DistilBERT on CPUs
Haihao Shen, Ofir Zafrir, Bo Dong +7
Transformer-based language models have become the standard approach to solving natural language processing tasks. However, industry adoption usually requires the maximum throughput…
A Brief History of Recommender Systems
Zhenhua Dong, Zhe Wang, Jun Xu +2
Soon after the invention of the Internet, the recommender system emerged and related technologies have been extensively studied and applied by both academia and industry. Currently…
Identifying and Exploiting Sparse Branch Correlations for Optimizing Branch Prediction
Anastasios Zouzias, Kleovoulos Kalaitzidis, Konstantin Berestizshevsky +3
Branch prediction is arguably one of the most important speculative mechanisms within a high-performance processor architecture. A common approach to improve branch prediction accu…
POSO: Personalized Cold Start Modules for Large-scale Recommender Systems
Shangfeng Dai, Haobin Lin, Zhichen Zhao +5
Recommendation for new users, also called user cold start, has been a well-recognized challenge for online recommender systems. Most existing methods view the crux as the lack of i…
COLD: Towards the Next Generation of Pre-Ranking System
Zhe Wang, Liqin Zhao, Biye Jiang +3
Multi-stage cascade architecture exists widely in many industrial systems such as recommender systems and online advertising, which often consists of sequential modules including m…
Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
Pi Qi, Xiaoqiang Zhu, Guorui Zhou +5
Rich user behavior data has been proven to be of great value for click-through rate prediction tasks, especially in industrial applications such as recommender systems and online a…
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction
Kun Gai, Xiaoqiang Zhu, Han Li +2
CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data. In this paper, we introduce an industrial strength solution wi…