Search Results for author: Keping Yang

Found 14 papers, 6 papers with code

Modeling User Behavior with Graph Convolution for Personalized Product Search

1 code implementation12 Feb 2022 Fan Lu, Qimai Li, Bo Liu, Xiao-Ming Wu, Xiaotong Zhang, Fuyu Lv, Guli Lin, Sen Li, Taiwei Jin, Keping Yang

Our approach can be seamlessly integrated with existing latent space based methods and be potentially applied in any product retrieval method that uses purchase history to model user preferences.

Learning Semantic Representations

Embedding-based Product Retrieval in Taobao Search

no code implementations17 Jun 2021 Sen Li, Fuyu Lv, Taiwei Jin, Guli Lin, Keping Yang, Xiaoyi Zeng, Xiao-Ming Wu, Qianli Ma

We evaluate MGDSPR on Taobao Product Search with significant metrics gains observed in offline experiments and online A/B tests.

AutoDebias: Learning to Debias for Recommendation

1 code implementation10 May 2021 Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin, Keping Yang

This provides a valuable opportunity to develop a universal solution for debiasing, e. g., by learning the debiasing parameters from data.

Imputation Meta-Learning +1

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

M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems

1 code implementation20 May 2020 Menghan Wang, Yujie Lin, Guli Lin, Keping Yang, Xiao-Ming Wu

Most existing methods can be categorized as \emph{multi-view representation fusion}; they first build one graph and then integrate multi-view data into a single compact representation for each node in the graph.

Graph Representation Learning Inductive Bias +2

AliCoCo: Alibaba E-commerce Cognitive Concept Net

no code implementations30 Mar 2020 Xusheng Luo, Luxin Liu, Yonghua Yang, Le Bo, Yuanpeng Cao, Jinhang Wu, Qiang Li, Keping Yang, Kenny Q. Zhu

However, user needs in e-commerce are still not well defined, and none of the existing ontologies has the enough depth and breadth for universal user needs understanding.

Tracing the Propagation Path: A Flow Perspective of Representation Learning on Graphs

no code implementations12 Dec 2019 Menghan Wang, Kun Zhang, Gulin Li, Keping Yang, Luo Si

We generalize the propagation strategies of current GCNs as a \emph{"Sink$\to$Source"} mode, which seems to be an underlying cause of the two challenges.

Representation Learning

Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction

no code implementations15 Oct 2019 Hong Wen, Jing Zhang, Yu-An Wang, Fuyu Lv, Wentian Bao, Quan Lin, Keping Yang

Although existing methods, typically built on the user sequential behavior path ``impression$\to$click$\to$purchase'', is effective for dealing with SSB issue, they still struggle to address the DS issue due to rare purchase training samples.

Click-Through Rate Prediction Multi-Task Learning +2

Conceptualize and Infer User Needs in E-commerce

1 code implementation8 Oct 2019 Xusheng Luo, Yonghua Yang, Kenny Q. Zhu, Yu Gong, Keping Yang

Understanding latent user needs beneath shopping behaviors is critical to e-commercial applications.

SDM: Sequential Deep Matching Model for Online Large-scale Recommender System

2 code implementations1 Sep 2019 Fuyu Lv, Taiwei Jin, Changlong Yu, Fei Sun, Quan Lin, Keping Yang, Wilfred Ng

In this paper, we propose a new sequential deep matching (SDM) model to capture users' dynamic preferences by combining short-term sessions and long-term behaviors.

Collaborative Filtering Recommendation Systems

User Validation of Recommendation Serendipity Metrics

no code implementations27 Jun 2019 Li Chen, Ningxia Wang, Yonghua Yang, Keping Yang, Quan Yuan

Though it has been recognized that recommending serendipitous (i. e., surprising and relevant) items can be helpful for increasing users' satisfaction and behavioral intention, how to measure serendipity in the offline environment is still an open issue.

Deep Session Interest Network for Click-Through Rate Prediction

6 code implementations16 May 2019 Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, Keping Yang

Easy-to-use, Modular and Extendible package of deep-learning based CTR models. DeepFM, DeepInterestNetwork(DIN), DeepInterestEvolutionNetwork(DIEN), DeepCrossNetwork(DCN), AttentionalFactorizationMachine(AFM), Neural Factorization Machine(NFM), AutoInt, Deep Session Interest Network(DSIN)

Click-Through Rate Prediction Recommendation Systems

Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System

no code implementations24 May 2018 Hong Wen, Jing Zhang, Quan Lin, Keping Yang, Pipei Huang

The deep cascade structure and the combination rule enable the proposed \textit{ldcTree} to have a stronger distributed feature representation ability.

Click-Through Rate Prediction Ensemble Learning

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