Zobrazeno 1 - 10
of 1 503
pro vyhledávání: '"Wang Songlin"'
Advancing Re-Ranking with Multimodal Fusion and Target-Oriented Auxiliary Tasks in E-Commerce Search
Autor:
Xu, Enqiang, Li, Xinhui, Zhou, Zhigong, Ji, Jiahao, Zhao, Jinyuan, Miao, Dadong, Wang, Songlin, Liu, Lin, Xu, Sulong
In the rapidly evolving field of e-commerce, the effectiveness of search re-ranking models is crucial for enhancing user experience and driving conversion rates. Despite significant advancements in feature representation and model architecture, the i
Externí odkaz:
http://arxiv.org/abs/2408.05751
Autor:
Kuai, Zhirui, Chen, Zuxu, Wang, Huimu, Li, Mingming, Miao, Dadong, Wang, Binbin, Chen, Xusong, Kuang, Li, Han, Yuxing, Wang, Jiaxing, Tang, Guoyu, Liu, Lin, Wang, Songlin, Zhuo, Jingwei
Generative retrieval (GR) has emerged as a transformative paradigm in search and recommender systems, leveraging numeric-based identifier representations to enhance efficiency and generalization. Notably, methods like TIGER employing Residual Quantiz
Externí odkaz:
http://arxiv.org/abs/2407.21488
Autor:
Cheng, Peng, Wang, Huimu, Zhao, Jinyuan, Wang, Yihao, Xu, Enqiang, Zhao, Yu, Xiao, Zhuojian, Wang, Songlin, Tang, Guoyu, Liu, Lin, Xu, Sulong
Traffic allocation is a process of redistributing natural traffic to products by adjusting their positions in the post-search phase, aimed at effectively fostering merchant growth, precisely meeting customer demands, and ensuring the maximization of
Externí odkaz:
http://arxiv.org/abs/2407.15476
Autor:
Wang, Huimu, Li, Mingming, Miao, Dadong, Wang, Songlin, Tang, Guoyu, Liu, Lin, Xu, Sulong, Hu, Jinghe
Re-ranking is a process of rearranging ranking list to more effectively meet user demands by accounting for the interrelationships between items. Existing methods predominantly enhance the precision of search results, often at the expense of diversit
Externí odkaz:
http://arxiv.org/abs/2405.15521
Autor:
Xu, Enqiang, Qiu, Yiming, Bai, Junyang, Zhang, Ping, Miao, Dadong, Wang, Songlin, Tang, Guoyu, Liu, Lin, Li, Mingming
In large e-commerce platforms, search systems are typically composed of a series of modules, including recall, pre-ranking, and ranking phases. The pre-ranking phase, serving as a lightweight module, is crucial for filtering out the bulk of products
Externí odkaz:
http://arxiv.org/abs/2405.05606
Query intent classification, which aims at assisting customers to find desired products, has become an essential component of the e-commerce search. Existing query intent classification models either design more exquisite models to enhance the repres
Externí odkaz:
http://arxiv.org/abs/2303.15870
Autor:
Wang, Binbin, Li, Mingming, Zeng, Zhixiong, Zhuo, Jingwei, Wang, Songlin, Xu, Sulong, Long, Bo, Yan, Weipeng
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. These methods adopt a two-tower framework to learn
Externí odkaz:
http://arxiv.org/abs/2303.11009
In this paper, we propose a robust multilingual model to improve the quality of search results. Our model not only leverage the processed class-balanced dataset, but also benefit from multitask pre-training that leads to more general representations.
Externí odkaz:
http://arxiv.org/abs/2301.13455
Autor:
Qiu, Yiming, Zhao, Chenyu, Zhang, Han, Zhuo, Jingwei, Li, Tianhao, Zhang, Xiaowei, Wang, Songlin, Xu, Sulong, Long, Bo, Yang, Wen-Yun
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. Ho
Externí odkaz:
http://arxiv.org/abs/2208.06150
Publikováno v:
In Journal of Alloys and Compounds 25 November 2024 1006
