Search Results for author: Sulong Xu

Found 19 papers, 4 papers with code

A Unified Search and Recommendation Framework Based on Multi-Scenario Learning for Ranking in E-commerce

no code implementations17 May 2024 Jinhan Liu, Qiyu Chen, Junjie Xu, Junjie Li, Baoli Li, Sulong Xu

These issues can result in the suboptimal performance of multi-scenario models in handling both S&R scenarios.

Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation

no code implementations7 Jun 2023 Gangyi Zhang, Chongming Gao, Wenqiang Lei, Xiaojie Guo, Shijun Li, Hongshen Chen, Zhuozhi Ding, Sulong Xu, Lingfei Wu

In the VPMCR setting, we propose a solution called Adaptive Vague Preference Policy Learning (AVPPL), which consists of two components: Ambiguity-aware Soft Estimation (ASE) and Dynamism-aware Policy Learning (DPL).

Decision Making Recommendation Systems

JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions

1 code implementation24 May 2023 Jiongnan Liu, Zhicheng Dou, Guoyu Tang, Sulong Xu

To evaluate the effectiveness of these models, previous studies mainly utilize the simulated Amazon recommendation dataset, which contains automatically generated queries and excludes cold users and tail products.

A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval

no code implementations28 Mar 2023 Chunyuan Yuan, Yiming Qiu, Mingming Li, Haiqing Hu, Songlin Wang, Sulong Xu

However, these models cannot capture multi-granularity matching features from queries and categories, which makes them hard to mitigate the gap in the expression between informal queries and categories.

intent-classification Intent Classification +2

Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search

no code implementations20 Mar 2023 Binbin Wang, Mingming Li, Zhixiong Zeng, Jingwei Zhuo, Songlin Wang, Sulong Xu, Bo Long, Weipeng Yan

Retrieving relevant items that match users' queries from billion-scale corpus forms the core of industrial e-commerce search systems, in which embedding-based retrieval (EBR) methods are prevailing.


Face Clustering via Graph Convolutional Networks with Confidence Edges

no code implementations ICCV 2023 Yang Wu, Zhiwei Ge, Yuhao Luo, Lin Liu, Sulong Xu

Experiments show that our method outperforms existing methods on the face and person datasets to achieve state-of-the-art.

Clustering Face Clustering

Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search

1 code implementation12 Aug 2022 Yiming Qiu, Chenyu Zhao, Han Zhang, Jingwei Zhuo, TianHao Li, Xiaowei Zhang, Songlin Wang, Sulong Xu, Bo Long, Wen-Yun Yang

BERT-style models pre-trained on the general corpus (e. g., Wikipedia) and fine-tuned on specific task corpus, have recently emerged as breakthrough techniques in many NLP tasks: question answering, text classification, sequence labeling and so on.

Intent Detection Question Answering +3

Sequential Search with Off-Policy Reinforcement Learning

no code implementations1 Feb 2022 Dadong Miao, Yanan Wang, Guoyu Tang, Lin Liu, Sulong Xu, Bo Long, Yun Xiao, Lingfei Wu, Yunjiang Jiang

Recent years have seen a significant amount of interests in Sequential Recommendation (SR), which aims to understand and model the sequential user behaviors and the interactions between users and items over time.

reinforcement-learning Reinforcement Learning (RL) +1

SearchGCN: Powering Embedding Retrieval by Graph Convolution Networks for E-Commerce Search

no code implementations1 Jul 2021 Xinlin Xia, Shang Wang, Han Zhang, Songlin Wang, Sulong Xu, Yun Xiao, Bo Long, Wen-Yun Yang

Graph convolution networks (GCN), which recently becomes new state-of-the-art method for graph node classification, recommendation and other applications, has not been successfully applied to industrial-scale search engine yet.

Node Classification Retrieval

Joint Learning of Deep Retrieval Model and Product Quantization based Embedding Index

1 code implementation9 May 2021 Han Zhang, Hongwei Shen, Yiming Qiu, Yunjiang Jiang, Songlin Wang, Sulong Xu, Yun Xiao, Bo Long, Wen-Yun Yang

Embedding index that enables fast approximate nearest neighbor(ANN) search, serves as an indispensable component for state-of-the-art deep retrieval systems.

Quantization Retrieval

Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search

no code implementations13 Jan 2021 Ziyang Liu, Zhaomeng Cheng, Yunjiang Jiang, Yue Shang, Wei Xiong, Sulong Xu, Bo Long, Di Jin

We propose in this paper a novel Second-order Relevance, which is fundamentally different from the previous First-order Relevance, to improve result relevance prediction.

Network Embedding

Kalman Filtering Attention for User Behavior Modeling in CTR Prediction

no code implementations NeurIPS 2020 Hu Liu, Jing Lu, Xiwei Zhao, Sulong Xu, Hao Peng, Yutong Liu, Zehua Zhang, Jian Li, Junsheng Jin, Yongjun Bao, Weipeng Yan

First, conventional attentions mostly limit the attention field only to a single user's behaviors, which is not suitable in e-commerce where users often hunt for new demands that are irrelevant to any historical behaviors.

Click-Through Rate Prediction

Category-Specific CNN for Visual-aware CTR Prediction at

no code implementations18 Jun 2020 Hu Liu, Jing Lu, Hao Yang, Xiwei Zhao, Sulong Xu, Hao Peng, Zehua Zhang, Wenjie Niu, Xiaokun Zhu, Yongjun Bao, Weipeng Yan

Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR.

Click-Through Rate Prediction

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