1 code implementation • 21 Apr 2024 • Kelong Mao, Chenlong Deng, Haonan Chen, Fengran Mo, Zheng Liu, Tetsuya Sakai, Zhicheng Dou
Conversational search requires accurate interpretation of user intent from complex multi-turn contexts.
1 code implementation • 17 Mar 2024 • Fengran Mo, Bole Yi, Kelong Mao, Chen Qu, Kaiyu Huang, Jian-Yun Nie
Conversational search provides a more convenient interface for users to search by allowing multi-turn interaction with the search engine.
no code implementations • 20 Feb 2024 • Yiruo Cheng, Kelong Mao, Zhicheng Dou
Such transformation is achieved by training a recently proposed Vec2Text model based on the ad-hoc query encoder, leveraging the fact that the session and query embeddings share the same space in existing conversational dense retrieval.
no code implementations • 15 Feb 2024 • Hongjin Qian, Zheng Liu, Kelong Mao, Yujia Zhou, Zhicheng Dou
These strategies not only improve the efficiency of the retrieval process but also ensure that the fidelity of the generated grounding text evidence is maintained.
no code implementations • 11 Feb 2024 • Haonan Chen, Zhicheng Dou, Kelong Mao, Jiongnan Liu, Ziliang Zhao
Conversational search utilizes muli-turn natural language contexts to retrieve relevant passages.
1 code implementation • 30 Jan 2024 • Fengran Mo, Chen Qu, Kelong Mao, Tianyu Zhu, Zhan Su, Kaiyu Huang, Jian-Yun Nie
To address the aforementioned issues, we propose a History-Aware Conversational Dense Retrieval (HAConvDR) system, which incorporates two ideas: context-denoised query reformulation and automatic mining of supervision signals based on the actual impact of historical turns.
1 code implementation • 5 Jun 2023 • Fengran Mo, Jian-Yun Nie, Kaiyu Huang, Kelong Mao, Yutao Zhu, Peng Li, Yang Liu
An effective way to improve retrieval effectiveness is to expand the current query with historical queries.
1 code implementation • 25 May 2023 • Fengran Mo, Kelong Mao, Yutao Zhu, Yihong Wu, Kaiyu Huang, Jian-Yun Nie
In this paper, we propose ConvGQR, a new framework to reformulate conversational queries based on generative pre-trained language models (PLMs), one for query rewriting and another for generating potential answers.
4 code implementations • 3 Apr 2023 • Kelong Mao, Jieming Zhu, Liangcai Su, Guohao Cai, Yuru Li, Zhenhua Dong
As such, many two-stream interaction models (e. g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction.
Ranked #2 on Click-Through Rate Prediction on MovieLens
2 code implementations • 12 Mar 2023 • Kelong Mao, Zhicheng Dou, Fengran Mo, Jiewen Hou, Haonan Chen, Hongjin Qian
Precisely understanding users' contextual search intent has been an important challenge for conversational search.
2 code implementations • 28 Oct 2021 • Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, Xiuqiang He
In this paper, we take one step further to propose an ultra-simplified formulation of GCNs (dubbed UltraGCN), which skips infinite layers of message passing for efficient recommendation.
Ranked #3 on Recommendation Systems on Gowalla
1 code implementation • 26 Sep 2021 • Kelong Mao, Jieming Zhu, Jinpeng Wang, Quanyu Dai, Zhenhua Dong, Xi Xiao, Xiuqiang He
While many existing studies focus on the design of more powerful interaction encoders, the impacts of loss functions and negative sampling ratios have not yet been well explored.
Ranked #3 on Recommendation Systems on Yelp2018
no code implementations • 26 Aug 2020 • Kelong Mao, Xi Xiao, Jieming Zhu, Biao Lu, Ruiming Tang, Xiuqiang He
In this work, we propose to formulate item tagging as a link prediction problem between item nodes and tag nodes.
no code implementations • 25 Sep 2019 • Kelong Mao, Peilin Zhao, Tingyang Xu, Yu Rong, Xi Xiao, Junzhou Huang
With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers.
Ranked #17 on Single-step retrosynthesis on USPTO-50k