Search Results for author: Hongbo Deng

Found 15 papers, 3 papers with code

GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Model

no code implementations23 May 2022 Wenbo Su, Yuanxing Zhang, Yufeng Cai, Kaixu Ren, Pengjie Wang, Huimin Yi, Yue Song, Jing Chen, Hongbo Deng, Jian Xu, Lin Qu, Bo Zheng

Compared to the state-of-the-art derived asynchronous training, GBA achieves up to 0. 2% improvement on the AUC metric, which is significant for the recommendation models.

Recommendation Systems

Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction

no code implementations21 Dec 2021 Kailun Wu, Zhangming Chan, Weijie Bian, Lejian Ren, Shiming Xiang, Shuguang Han, Hongbo Deng, Bo Zheng

We further show that such a process is equivalent to adding an adversarial perturbation to the model input, and thereby name our proposed approach as an the Adversarial Gradient Driven Exploration (AGE).

Click-Through Rate Prediction online learning +1

Comparative Explanations of Recommendations

no code implementations1 Nov 2021 Aobo Yang, Nan Wang, Renqin Cai, Hongbo Deng, Hongning Wang

As recommendation is essentially a comparative (or ranking) process, a good explanation should illustrate to users why an item is believed to be better than another, i. e., comparative explanations about the recommended items.

Recommendation Systems

DemiNet: Dependency-Aware Multi-Interest Network with Self-Supervised Graph Learning for Click-Through Rate Prediction

no code implementations26 Sep 2021 Yule Wang, Qiang Luo, Yue Ding, Dong Wang, Hongbo Deng

In this paper, we propose a novel model named DemiNet (short for DEpendency-Aware Multi-Interest Network}) to address the above two issues.

Click-Through Rate Prediction Denoising +2

Path-based Deep Network for Candidate Item Matching in Recommenders

no code implementations18 May 2021 Houyi Li, Zhihong Chen, Chenliang Li, Rong Xiao, Hongbo Deng, Peng Zhang, Yongchao Liu, Haihong Tang

PDN utilizes Trigger Net to capture the user's interest in each of his/her interacted item, and Similarity Net to evaluate the similarity between each interacted item and the target item based on these items' profile and CF information.

Recommendation Systems

Learning a Product Relevance Model from Click-Through Data in E-Commerce

no code implementations14 Feb 2021 Shaowei Yao, Jiwei Tan, Xi Chen, Keping Yang, Rong Xiao, Hongbo Deng, Xiaojun Wan

We propose a novel way to consider samples of different relevance confidence, and come up with a new training objective to learn a robust relevance model with desirable score distribution.

Click-Through Rate Prediction

Explanation as a Defense of Recommendation

no code implementations24 Jan 2021 Aobo Yang, Nan Wang, Hongbo Deng, Hongning Wang

At training time, the two learning tasks are joined by a latent sentiment vector, which is encoded by the recommendation module and used to make word choices for explanation generation.

Explanation Generation

CAN: Feature Co-Action for Click-Through Rate Prediction

no code implementations11 Nov 2020 Weijie Bian, Kailun Wu, Lejian Ren, Qi Pi, Yujing Zhang, Can Xiao, Xiang-Rong Sheng, Yong-Nan Zhu, Zhangming Chan, Na Mou, Xinchen Luo, Shiming Xiang, Guorui Zhou, Xiaoqiang Zhu, Hongbo Deng

For example, a simple attempt to learn the combination of feature A and feature B <A, B> as the explicit cartesian product representation of new features can outperform previous implicit feature interaction models including factorization machine (FM)-based models and their variations.

Click-Through Rate Prediction

ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance

1 code implementation21 May 2020 Zhihong Chen, Rong Xiao, Chenliang Li, Gangfeng Ye, Haochuan Sun, Hongbo Deng

Most of ranking models are trained only with displayed items (most are hot items), but they are utilized to retrieve items in the entire space which consists of both displayed and non-displayed items (most are long-tail items).

Domain Adaptation Selection bias

CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network

1 code implementation21 May 2020 Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, Aixin Sun

Given two relevant domains (e. g., Book and Movie), users may have interactions with items in one domain but not in the other domain.

Recommendation Systems

AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search

1 code implementation13 Jan 2020 Daoyuan Chen, Yaliang Li, Minghui Qiu, Zhen Wang, Bofang Li, Bolin Ding, Hongbo Deng, Jun Huang, Wei. Lin, Jingren Zhou

Motivated by the necessity and benefits of task-oriented BERT compression, we propose a novel compression method, AdaBERT, that leverages differentiable Neural Architecture Search to automatically compress BERT into task-adaptive small models for specific tasks.

Knowledge Distillation Natural Language Processing +1

Gated Group Self-Attention for Answer Selection

no code implementations26 May 2019 Dong Xu, Jianhui Ji, Haikuan Huang, Hongbo Deng, Wu-Jun Li

Nevertheless, it is difficult for RNN based models to capture the information about long-range dependency among words in the sentences of questions and answers.

Answer Selection Machine Translation +1

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