Search Results for author: Binqiang Zhao

Found 15 papers, 2 papers with code

Residential Floor Plan Recognition and Reconstruction

no code implementations CVPR 2021 Xiaolei Lv, Shengchu Zhao, Xinyang Yu, Binqiang Zhao

Recognition and reconstruction of residential floor plan drawings are important and challenging in design, decoration, and architectural remodeling fields.

3D Reconstruction

Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning

no code implementations ICCV 2021 Ming-Xian Lin, Jie Yang, He Wang, Yu-Kun Lai, Rongfei Jia, Binqiang Zhao, Lin Gao

Inspired by the great success in recent contrastive learning works on self-supervised representation learning, we propose a novel IBSR pipeline leveraging contrastive learning.

3D Shape Retrieval Contrastive Learning +3

TPG-DNN: A Method for User Intent Prediction Based on Total Probability Formula and GRU Loss with Multi-task Learning

no code implementations5 Aug 2020 Jingxing Jiang, Zhubin Wang, Fei Fang, Binqiang Zhao

Critical as is to improve the online shopping experience for customers and merchants, how to find a proper approach for user intent prediction are paid great attention in both industry and academia.

Multi-Task Learning

Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records

no code implementations1 Aug 2020 Jiatu Shi, Huaxiu Yao, Xian Wu, Tong Li, Zedong Lin, Tengfei Wang, Binqiang Zhao

The goal is to facilitate the learning process in the target segments by leveraging the learned knowledge from data-sufficient source segments.


RNE: A Scalable Network Embedding for Billion-scale Recommendation

no code implementations10 Mar 2020 Jianbin Lin, Daixin Wang, Lu Guan, Yin Zhao, Binqiang Zhao, Jun Zhou, Xiaolong Li, Yuan Qi

However, due to the huge number of users and items, the diversity and dynamic property of the user interest, how to design a scalable recommendation system, which is able to efficiently produce effective and diverse recommendation results on billion-scale scenarios, is still a challenging and open problem for existing methods.

Network Embedding

PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation

1 code implementation IJCAI 2019 Qiong Wu, Yong liu, Chunyan Miao, Binqiang Zhao, Yin Zhao, Lu Guan

This paper proposes Personalized Diversity-promoting GAN (PD-GAN), a novel recommendation model to generate diverse, yet relevant recommendations.

Recommendation Systems

POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion

1 code implementation6 May 2019 Wen Chen, Pipei Huang, Jiaming Xu, Xin Guo, Cheng Guo, Fei Sun, Chao Li, Andreas Pfadler, Huan Zhao, Binqiang Zhao

In particular, there exist two requirements for fashion outfit recommendation: the Compatibility of the generated fashion outfits, and the Personalization in the recommendation process.

Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation

no code implementations19 Mar 2019 Yong Liu, Yinan Zhang, Qiong Wu, Chunyan Miao, Lizhen Cui, Binqiang Zhao, Yin Zhao, Lu Guan

Interactive recommendation that models the explicit interactions between users and the recommender system has attracted a lot of research attentions in recent years.

Recommendation Systems

Deep Bayesian Multi-Target Learning for Recommender Systems

no code implementations25 Feb 2019 Qi. Wang, Zhihui Ji, Huasheng Liu, Binqiang Zhao

This work introduces a multi-target optimization framework with Bayesian modeling of the target events, called Deep Bayesian Multi-Target Learning (DBMTL).

Model Selection Recommendation Systems

Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba

no code implementations6 Mar 2018 Jizhe Wang, Pipei Huang, Huan Zhao, Zhibo Zhang, Binqiang Zhao, Dik Lun Lee

Using online A/B test, we show that the online Click-Through-Rate (CTRs) are improved comparing to the previous recommendation methods widely used in Taobao, further demonstrating the effectiveness and feasibility of our proposed methods in Taobao's live production environment.

Graph Embedding Recommendation Systems

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