Search Results for author: Suqi Cheng

Found 13 papers, 4 papers with code

Text-Video Retrieval via Variational Multi-Modal Hypergraph Networks

no code implementations6 Jan 2024 Qian Li, Lixin Su, Jiashu Zhao, Long Xia, Hengyi Cai, Suqi Cheng, Hengzhu Tang, Junfeng Wang, Dawei Yin

Compared to conventional textual retrieval, the main obstacle for text-video retrieval is the semantic gap between the textual nature of queries and the visual richness of video content.

Retrieval Variational Inference +1

Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM

no code implementations24 Dec 2023 Xiaopeng Li, Lixin Su, Pengyue Jia, Xiangyu Zhao, Suqi Cheng, Junfeng Wang, Dawei Yin

To be specific, we use Chain of Thought (CoT) technology to utilize Large Language Models (LLMs) as agents to emulate various demographic profiles, then use them for efficient query rewriting, and we innovate a robust Multi-gate Mixture of Experts (MMoE) architecture coupled with a hybrid loss function, collectively strengthening the ranking models' robustness.

LLMRec: Large Language Models with Graph Augmentation for Recommendation

1 code implementation1 Nov 2023 Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, Chao Huang

By employing these strategies, we address the challenges posed by sparse implicit feedback and low-quality side information in recommenders.

Model Optimization Recommendation Systems

Representation Learning with Large Language Models for Recommendation

1 code implementation24 Oct 2023 Xubin Ren, Wei Wei, Lianghao Xia, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, Chao Huang

RLMRec incorporates auxiliary textual signals, develops a user/item profiling paradigm empowered by LLMs, and aligns the semantic space of LLMs with the representation space of collaborative relational signals through a cross-view alignment framework.

Recommendation Systems Representation Learning

GraphGPT: Graph Instruction Tuning for Large Language Models

1 code implementation19 Oct 2023 Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, Chao Huang

In this work, we present the GraphGPT framework that aligns LLMs with graph structural knowledge with a graph instruction tuning paradigm.

Data Augmentation Graph Learning +2

Approximated Doubly Robust Search Relevance Estimation

no code implementations16 Aug 2022 Lixin Zou, Changying Hao, Hengyi Cai, Suqi Cheng, Shuaiqiang Wang, Wenwen Ye, Zhicong Cheng, Simiu Gu, Dawei Yin

We further instantiate the proposed unbiased relevance estimation framework in Baidu search, with comprehensive practical solutions designed regarding the data pipeline for click behavior tracking and online relevance estimation with an approximated deep neural network.


Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking

no code implementations25 Apr 2022 Qian Dong, Yiding Liu, Suqi Cheng, Shuaiqiang Wang, Zhicong Cheng, Shuzi Niu, Dawei Yin

To leverage a reliable knowledge, we propose a novel knowledge graph distillation method and obtain a knowledge meta graph as the bridge between query and passage.

Natural Language Understanding Passage Re-Ranking +2

Graph Enhanced BERT for Query Understanding

no code implementations3 Apr 2022 Juanhui Li, Yao Ma, Wei Zeng, Suqi Cheng, Jiliang Tang, Shuaiqiang Wang, Dawei Yin

In other words, GE-BERT can capture both the semantic information and the users' search behavioral information of queries.

Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate Estimation

1 code implementation28 May 2021 Siyuan Guo, Lixin Zou, Yiding Liu, Wenwen Ye, Suqi Cheng, Shuaiqiang Wang, Hechang Chen, Dawei Yin, Yi Chang

Based on it, a more robust doubly robust (MRDR) estimator has been proposed to further reduce its variance while retaining its double robustness.

counterfactual Imputation +2

Pre-trained Language Model based Ranking in Baidu Search

no code implementations24 May 2021 Lixin Zou, Shengqiang Zhang, Hengyi Cai, Dehong Ma, Suqi Cheng, Daiting Shi, Zhifan Zhu, Weiyue Su, Shuaiqiang Wang, Zhicong Cheng, Dawei Yin

However, it is nontrivial to directly apply these PLM-based rankers to the large-scale web search system due to the following challenging issues:(1) the prohibitively expensive computations of massive neural PLMs, especially for long texts in the web-document, prohibit their deployments in an online ranking system that demands extremely low latency;(2) the discrepancy between existing ranking-agnostic pre-training objectives and the ad-hoc retrieval scenarios that demand comprehensive relevance modeling is another main barrier for improving the online ranking system;(3) a real-world search engine typically involves a committee of ranking components, and thus the compatibility of the individually fine-tuned ranking model is critical for a cooperative ranking system.

Language Modelling Retrieval

IMRank: Influence Maximization via Finding Self-Consistent Ranking

no code implementations17 Feb 2014 Suqi Cheng, Hua-Wei Shen, Junming Huang, Wei Chen, Xue-Qi Cheng

Early methods mainly fall into two paradigms with certain benefits and drawbacks: (1)Greedy algorithms, selecting seed nodes one by one, give a guaranteed accuracy relying on the accurate approximation of influence spread with high computational cost; (2)Heuristic algorithms, estimating influence spread using efficient heuristics, have low computational cost but unstable accuracy.

Social and Information Networks Data Structures and Algorithms F.2.2; D.2.8

StaticGreedy: solving the scalability-accuracy dilemma in influence maximization

no code implementations19 Dec 2012 Suqi Cheng, Hua-Wei Shen, Junming Huang, Guoqing Zhang, Xue-Qi Cheng

We point out that the essential reason of the dilemma is the surprising fact that the submodularity, a key requirement of the objective function for a greedy algorithm to approximate the optimum, is not guaranteed in all conventional greedy algorithms in the literature of influence maximization.

Social and Information Networks Data Structures and Algorithms Physics and Society F.2.2; D.2.8

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