Zobrazeno 1 - 10
of 603
pro vyhledávání: '"Lu, Hanqing"'
Autor:
Luo, Chen, Tang, Xianfeng, Lu, Hanqing, Xie, Yaochen, Liu, Hui, Dai, Zhenwei, Cui, Limeng, Joshi, Ashutosh, Nag, Sreyashi, Li, Yang, Li, Zhen, Goutam, Rahul, Tang, Jiliang, Zhang, Haiyang, He, Qi
Online shopping platforms, such as Amazon, offer services to billions of people worldwide. Unlike web search or other search engines, product search engines have their unique characteristics, primarily featuring short queries which are mostly a combi
Externí odkaz:
http://arxiv.org/abs/2408.02215
Autor:
Zeng, Shenglai, Zhang, Jiankun, He, Pengfei, Ren, Jie, Zheng, Tianqi, Lu, Hanqing, Xu, Han, Liu, Hui, Xing, Yue, Tang, Jiliang
Retrieval-augmented generation (RAG) enhances the outputs of language models by integrating relevant information retrieved from external knowledge sources. However, when the retrieval process involves private data, RAG systems may face severe privacy
Externí odkaz:
http://arxiv.org/abs/2406.14773
Autor:
Han, Haoyu, Li, Juanhui, Huang, Wei, Tang, Xianfeng, Lu, Hanqing, Luo, Chen, Liu, Hui, Tang, Jiliang
Graph Neural Networks (GNNs) have proven to be highly effective for node classification tasks across diverse graph structural patterns. Traditionally, GNNs employ a uniform global filter, typically a low-pass filter for homophilic graphs and a high-p
Externí odkaz:
http://arxiv.org/abs/2406.03464
Autor:
Wei, Tianxin, Jin, Bowen, Li, Ruirui, Zeng, Hansi, Wang, Zhengyang, Sun, Jianhui, Yin, Qingyu, Lu, Hanqing, Wang, Suhang, He, Jingrui, Tang, Xianfeng
Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration. Our daily choices, especially in domains like fashion and retail, are su
Externí odkaz:
http://arxiv.org/abs/2403.10667
Autor:
Jin, Bowen, Zeng, Hansi, Wang, Guoyin, Chen, Xiusi, Wei, Tianxin, Li, Ruirui, Wang, Zhengyang, Li, Zheng, Li, Yang, Lu, Hanqing, Wang, Suhang, Han, Jiawei, Tang, Xianfeng
Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs. Previous studies typically adopt a two-stage pipeline to learn semantic IDs by firs
Externí odkaz:
http://arxiv.org/abs/2310.07815
Autor:
Jin, Wei, Mao, Haitao, Li, Zheng, Jiang, Haoming, Luo, Chen, Wen, Hongzhi, Han, Haoyu, Lu, Hanqing, Wang, Zhengyang, Li, Ruirui, Li, Zhen, Cheng, Monica Xiao, Goutam, Rahul, Zhang, Haiyang, Subbian, Karthik, Wang, Suhang, Sun, Yizhou, Tang, Jiliang, Yin, Bing, Tang, Xianfeng
Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences is essential for providing personalized recommendations. Session-based
Externí odkaz:
http://arxiv.org/abs/2307.09688
Autor:
Huang, Zijie, Li, Zheng, Jiang, Haoming, Cao, Tianyu, Lu, Hanqing, Yin, Bing, Subbian, Karthik, Sun, Yizhou, Wang, Wei
Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete. Due to labor-intensive human labeling, this phenomenon deteriorates when handling knowledge represented in various languages. In this paper, we explore
Externí odkaz:
http://arxiv.org/abs/2203.14987
Autor:
Xie, Jiayin, Wang, Xiaopan, Lin, Jing, Kong, Sifang, Lu, Hanqing, Liu, Zili, Wang, Qiying, Zuo, Jianliang, Hu, Fei, Zeng, Zhiwei
Publikováno v:
In Chemical Engineering Journal 1 October 2024 497
Autor:
Zhang, Danqing, Li, Zheng, Cao, Tianyu, Luo, Chen, Wu, Tony, Lu, Hanqing, Song, Yiwei, Yin, Bing, Zhao, Tuo, Yang, Qiang
Publikováno v:
The 30th ACM International Conference on Information and Knowledge Management (CIKM 2021, Applied Research Track)
We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform them into formally canonical forms. Such a problem consists of two phas
Externí odkaz:
http://arxiv.org/abs/2108.08468
Autor:
Liu, Jing, Zhu, Xinxin, Liu, Fei, Guo, Longteng, Zhao, Zijia, Sun, Mingzhen, Wang, Weining, Lu, Hanqing, Zhou, Shiyu, Zhang, Jiajun, Wang, Jinqiao
In this paper, we propose an Omni-perception Pre-Trainer (OPT) for cross-modal understanding and generation, by jointly modeling visual, text and audio resources. OPT is constructed in an encoder-decoder framework, including three single-modal encode
Externí odkaz:
http://arxiv.org/abs/2107.00249