Search Results for author: Fuyu Lv

Found 15 papers, 6 papers with code

Cold-Start based Multi-Scenario Ranking Model for Click-Through Rate Prediction

no code implementations16 Apr 2023 Peilin Chen, Hong Wen, Jing Zhang, Fuyu Lv, Zhao Li, Qijie Shen, Wanjie Tao, Ying Zhou, Chao Zhang

Online travel platforms (OTPs), e. g., Ctrip. com or Fliggy. com, can effectively provide travel-related products or services to users.

Click-Through Rate Prediction

Delving into E-Commerce Product Retrieval with Vision-Language Pre-training

no code implementations10 Apr 2023 Xiaoyang Zheng, Fuyu Lv, Zilong Wang, Qingwen Liu, Xiaoyi Zeng

E-commerce search engines comprise a retrieval phase and a ranking phase, where the first one returns a candidate product set given user queries.

Contrastive Learning Retrieval

Re-weighting Negative Samples for Model-Agnostic Matching

no code implementations6 Jul 2022 Jiazhen Lou, Hong Wen, Fuyu Lv, Jing Zhang, Tengfei Yuan, Zhao Li

Recommender Systems (RS), as an efficient tool to discover users' interested items from a very large corpus, has attracted more and more attention from academia and industry.

Multi-Task Learning Recommendation Systems

Intelligent Request Strategy Design in Recommender System

no code implementations23 Jun 2022 Xufeng Qian, Yue Xu, Fuyu Lv, Shengyu Zhang, Ziwen Jiang, Qingwen Liu, Xiaoyi Zeng, Tat-Seng Chua, Fei Wu

RSs typically put a large number of items into one page to reduce excessive resource consumption from numerous paging requests, which, however, would diminish the RSs' ability to timely renew the recommendations according to users' real-time interest and lead to a poor user experience.

Causal Inference counterfactual +1

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 Retrieval

IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search

1 code implementation10 Feb 2022 Dian Cheng, Jiawei Chen, Wenjun Peng, Wenqin Ye, Fuyu Lv, Tao Zhuang, Xiaoyi Zeng, Xiangnan He

On this basis, we develop a specific interactive hypergraph neural network to explicitly encode the structure information (i. e., collaborative signal) into the embedding process.

Representation Learning

Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation

1 code implementation5 Feb 2022 Qijie Shen, Hong Wen, Wanjie Tao, Jing Zhang, Fuyu Lv, Zulong Chen, Zhao Li

In many classical e-commerce platforms, personalized recommendation has been proven to be of great business value, which can improve user satisfaction and increase the revenue of platforms.

Click-Through Rate Prediction

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.

Open-Ended Question Answering Retrieval

Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction

no code implementations20 Apr 2021 Hong Wen, Jing Zhang, Fuyu Lv, Wentian Bao, Tianyi Wang, Zulong Chen

Motivated by this observation, we propose a novel \emph{CVR} prediction method by Hierarchically Modeling both Micro and Macro behaviors ($HM^3$).

Multi-Task Learning Selection bias

XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System

1 code implementation24 Oct 2020 Fuyu Lv, Mengxue Li, Tonglei Guo, Changlong Yu, Fei Sun, Taiwei Jin, Wilfred Ng

The offline experimental results based on real-world E-commerce data demonstrate the effectiveness and verify the importance of unclicked items in sequential recommendation.

Metric Learning Retrieval +1

MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction

no code implementations13 Aug 2020 Yufei Feng, Fuyu Lv, Binbin Hu, Fei Sun, Kun Kuang, Yang Liu, Qingwen Liu, Wenwu Ou

In this paper, we propose a new framework named Multiplex Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex relations between user behaviors and target item to enhance CTR prediction.

Click-Through Rate Prediction Recommendation Systems +1

ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation

no code implementations25 May 2020 Yufei Feng, Binbin Hu, Fuyu Lv, Qingwen Liu, Zhiqiang Zhang, Wenwu Ou

Specifically, to associate the given target item with user behaviors over KG, we propose the graph connect and graph prune techniques to construct adaptive target-behavior relational graph.

Recommendation Systems

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

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

Deep Session Interest Network for Click-Through Rate Prediction

7 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

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