296 citations · 399 across the 14 of their papers we have counts for
14 papers
PEARL: Unbiased Percentile Estimation via Contrastive Learning for Industrial-Scale Livestream Recommendation
Blake Gella, Wei Wu, Yuhao Yin +6
Recommender systems trained on user interaction data are susceptible to behavioral intensity imbalance--a systematic distortion arising from heterogeneous engagement patterns acros…
DebCSE: Rethinking Unsupervised Contrastive Sentence Embedding Learning in the Debiasing Perspective
Pu Miao, Zeyao Du, Junlin Zhang
Several prior studies have suggested that word frequency biases can cause the Bert model to learn indistinguishable sentence embeddings. Contrastive learning schemes such as SimCSE…
Perception Test: A Diagnostic Benchmark for Multimodal Video Models
Viorica Pătrăucean, Lucas Smaira, Ankush Gupta +21
We propose a novel multimodal video benchmark - the Perception Test - to evaluate the perception and reasoning skills of pre-trained multimodal models (e.g. Flamingo, SeViLA, or GP…
MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction
Pengtao Zhang, Junlin Zhang
New findings in natural language processing (NLP) demonstrate that the strong memorization capability contributes a lot to the success of Large Language Models (LLM). This inspires…
FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction
Pengtao Zhang, Zheng Zheng, Junlin Zhang
Click-Through Rate (CTR) estimation has become one of the most fundamental tasks in many real-world applications and various deep models have been proposed. Some research has prove…
ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding
Zhiqiang Wang, Qingyun She, PengTao Zhang +1
Click-through rate (CTR) estimation is a fundamental task in personalized advertising and recommender systems and it's important for ranking models to effectively capture complex h…
Leaf-FM: A Learnable Feature Generation Factorization Machine for Click-Through Rate Prediction
Qingyun She, Zhiqiang Wang, Junlin Zhang
Click-through rate (CTR) prediction plays important role in personalized advertising and recommender systems. Though many models have been proposed such as FM, FFM and DeepFM in re…
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
Zhiqiang Wang, Qingyun She, Junlin Zhang
Click-Through Rate(CTR) estimation has become one of the most fundamental tasks in many real-world applications and it's important for ranking models to effectively capture complex…
BoostingBERT:Integrating Multi-Class Boosting into BERT for NLP Tasks
Tongwen Huang, Qingyun She, Junlin Zhang
As a pre-trained Transformer model, BERT (Bidirectional Encoder Representations from Transformers) has achieved ground-breaking performance on multiple NLP tasks. On the other hand…
Correct Normalization Matters: Understanding the Effect of Normalization On Deep Neural Network Models For Click-Through Rate Prediction
Zhiqiang Wang, Qingyun She, PengTao Zhang +1
Normalization has become one of the most fundamental components in many deep neural networks for machine learning tasks while deep neural network has also been widely used in CTR e…
GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction
Tongwen Huang, Qingyun She, Zhiqiang Wang +1
Advertising and feed ranking are essential to many Internet companies such as Facebook. Among many real-world advertising and feed ranking systems, click through rate (CTR) predict…
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
Tongwen Huang, Zhiqi Zhang, Junlin Zhang
Advertising and feed ranking are essential to many Internet companies such as Facebook and Sina Weibo. Among many real-world advertising and feed ranking systems, click through rat…
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
Junlin Zhang, Tongwen Huang, Zhiqi Zhang
Click through rate (CTR) estimation is a fundamental task in personalized advertising and recommender systems. Recent years have witnessed the success of both the deep learning bas…
Empirical Evaluation of RNN Architectures on Sentence Classification Task
Lei Shen, Junlin Zhang
Recurrent Neural Networks have achieved state-of-the-art results for many problems in NLP and two most popular RNN architectures are Tail Model and Pooling Model. In this paper, a…