Search Results for author: Qifang Zhao

Found 4 papers, 1 papers with code

UniMatch: A Unified User-Item Matching Framework for the Multi-purpose Merchant Marketing

no code implementations19 Jul 2023 Qifang Zhao, Tianyu Li, Meng Du, Yu Jiang, Qinghui Sun, Zhongyao Wang, Hong Liu, Huan Xu

When doing private domain marketing with cloud services, the merchants usually have to purchase different machine learning models for the multiple marketing purposes, leading to a very high cost.


TotalRecall: A Bidirectional Candidates Generation Framework for Large Scale Recommender \& Advertising Systems

no code implementations29 Sep 2021 Qifang Zhao, Yu Jiang, Yuqing Liu, Meng Du, Qinghui Sun, Chao Xu, Huan Xu, Zhongyao Wang

Recommender (RS) and Advertising/Marketing Systems (AS) play the key roles in E-commerce companies like Amazaon and Alibaba.


Learning to Profile: User Meta-Profile Network for Few-Shot Learning

no code implementations21 Aug 2020 Hao Gong, Qifang Zhao, Tianyu Li, Derek Cho, DuyKhuong Nguyen

1) Meta-learning model: In the context of representation learning with e-commerce user behavior data, we propose a meta-learning framework called the Meta-Profile Network, which extends the ideas of matching network and relation network for knowledge transfer and fast adaptation; 2) Encoding strategy: To keep high fidelity of large-scale long-term sequential behavior data, we propose a time-heatmap encoding strategy that allows the model to encode data effectively; 3) Deep network architecture: A multi-modal model combined with multi-task learning architecture is utilized to address the cross-domain knowledge learning and insufficient label problems.

Few-Shot Learning Multi-Task Learning +3

Learning Classifiers on Positive and Unlabeled Data with Policy Gradient

1 code implementation15 Oct 2019 Tianyu Li, Chien-Chih Wang, Yukun Ma, Patricia Ortal, Qifang Zhao, Bjorn Stenger, Yu Hirate

Existing algorithms aiming to learn a binary classifier from positive (P) and unlabeled (U) data generally require estimating the class prior or label noises ahead of building a classification model.

General Classification

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