Search Results for author: Kelong Mao

Found 14 papers, 9 papers with code

ConvSDG: Session Data Generation for Conversational Search

1 code implementation17 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.

Conversational Search Retrieval +1

Interpreting Conversational Dense Retrieval by Rewriting-Enhanced Inversion of Session Embedding

no code implementations20 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.

Conversational Search Retrieval

Grounding Language Model with Chunking-Free In-Context Retrieval

no code implementations15 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.

Chunking Language Modelling +2

History-Aware Conversational Dense Retrieval

1 code implementation30 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.

Conversational Search Information Retrieval +1

ConvGQR: Generative Query Reformulation for Conversational Search

1 code implementation25 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.

Conversational Search Retrieval

FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction

4 code implementations3 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.

Click-Through Rate Prediction feature selection +1

UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation

2 code implementations28 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.

Collaborative Filtering Recommendation Systems

SimpleX: A Simple and Strong Baseline for Collaborative Filtering

1 code implementation26 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.

Collaborative Filtering Recommendation Systems

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