activity
20172021
most citedEnd-to-End User Behavior Retrieval in Click-Through RatePrediction Model

17 citations · 28 across the 3 of their papers we have counts for

collaborators

5 papers

cs.IR202117 cited

End-to-End User Behavior Retrieval in Click-Through RatePrediction Model

Qiwei Chen, Changhua Pei, Shanshan Lv +3

Click-Through Rate (CTR) prediction is one of the core tasks in recommender systems (RS). It predicts a personalized click probability for each user-item pair. Recently, researcher…

cs.CL2019

Best Practices for Learning Domain-Specific Cross-Lingual Embeddings

Lena Shakurova, Beata Nyari, Chao Li +1

Cross-lingual embeddings aim to represent words in multiple languages in a shared vector space by capturing semantic similarities across languages. They are a crucial component for…

cs.CL20191 cited

Predicting Research Trends From Arxiv

Steffen Eger, Chao Li, Florian Netzer +1

We perform trend detection on two datasets of Arxiv papers, derived from its machine learning (cs.LG) and natural language processing (cs.CL) categories. Our approach is bottom-up:…

cs.CV201710 cited

Cascade Region Proposal and Global Context for Deep Object Detection

Qiaoyong Zhong, Chao Li, Yingying Zhang +3

Deep region-based object detector consists of a region proposal step and a deep object recognition step. In this paper, we make significant improvements on both of the two steps. F…

cs.CL2017

Deep Speaker: an End-to-End Neural Speaker Embedding System

Chao Li, Xiaokong Ma, Bing Jiang +6

We present Deep Speaker, a neural speaker embedding system that maps utterances to a hypersphere where speaker similarity is measured by cosine similarity. The embeddings generated…