Qingyao Ai

Structure-aware Relative Policy Optimization for Ranking

Yiteng Tu, Weihang Su, Zitao Su +3

Ranking is a fundamental component of modern information access systems. Reinforcement learning (RL) provides a flexible framework for directly optimizing coarse-grained feedback a…

Generative Chinese Statute Retrieval

Yiteng Tu, Zitao Su, Weihang Su +5

Statute retrieval is a fundamental task in legal information retrieval, yet existing approaches struggle to bridge the gap between colloquial legal queries and formal statutory lan…

Analytical Search

Yiteng Tu, Shuo Miao, Weihang Su +2

Analytical information needs, such as trend analysis and causal impact assessment, are prevalent across various domains including law, finance, science, and much more. However, exi…

Multi-Field Tool Retrieval

Yichen Tang, Weihang Su, Yiqun Liu +1

Integrating external tools enables Large Language Models (LLMs) to interact with real-world environments and solve complex tasks. Given the growing scale of available tools, effect…

Beyond Exposure: Optimizing Ranking Fairness with Non-linear Time-Income Functions

Xuancheng Li, Tao Yang, Yujia Zhou +2

Ranking systems in web search and recommendation allocate attention among items and providers, and therefore need to balance relevance-based effectiveness with provider fairness. E…

Equity vs. Equality: Optimizing Ranking Fairness for Tailored Provider Needs

Yiteng Tu, Weihang Su, Shuguang Han +2

Ranking plays a central role in connecting users and providers in Information Retrieval (IR) systems, making provider-side fairness an important challenge. While recent research ha…

Generalized Pseudo-Relevance Feedback

Yiteng Tu, Weihang Su, Yujia Zhou +4

Query rewriting is a fundamental technique in information retrieval (IR). It typically employs the retrieval result as relevance feedback to refine the query and thereby addresses…

Are Recommenders Self-Aware? Label-Free Recommendation Performance Estimation via Model Uncertainty

Jiayu Li, Ziyi Ye, Guohao Jian +4

Can a recommendation model be self-aware? This paper investigates the recommender's self-awareness by quantifying its uncertainty, which provides a label-free estimation of its per…

SelfRACG: Enabling LLMs to Self-Express and Retrieve for Code Generation

Qian Dong, Jia Chen, Qingyao Ai +6

Existing retrieval-augmented code generation (RACG) methods typically use an external retrieval module to fetch semantically similar code snippets used for generating subsequent fr…

Enhancing LLM-Based Agents via Global Planning and Hierarchical Execution

Junjie Chen, Haitao Li, Jingli Yang +2

Intelligent agent systems based on Large Language Models (LLMs) have shown great potential in real-world applications. However, existing agent frameworks still face critical limita…

Qilin: A Multimodal Information Retrieval Dataset with APP-level User Sessions

Jia Chen, Qian Dong, Haitao Li +9

User-generated content (UGC) communities, especially those featuring multimodal content, improve user experiences by integrating visual and textual information into results (or ite…

Foundations of GenIR

Qingyao Ai, Jingtao Zhan, Yiqun Liu

The chapter discusses the foundational impact of modern generative AI models on information access (IA) systems. In contrast to traditional AI, the large-scale training and superio…

Unsupervised dense retrieval with conterfactual contrastive learning

Haitian Chen, Qingyao Ai, Xiao Wang +3

Efficiently retrieving a concise set of candidates from a large document corpus remains a pivotal challenge in Information Retrieval (IR). Neural retrieval models, particularly den…

STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionals

Weihang Su, Yiran Hu, Anzhe Xie +6

Statute retrieval aims to find relevant statutory articles for specific queries. This process is the basis of a wide range of legal applications such as legal advice, automated jud…

EEG-SVRec: An EEG Dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation

Shaorun Zhang, Zhiyu He, Ziyi Ye +4

In recent years, short video platforms have gained widespread popularity, making the quality of video recommendations crucial for retaining users. Existing recommendation systems p…

Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval

Haitao Li, You Chen, Zhekai Ge +4

Legal retrieval techniques play an important role in preserving the fairness and equality of the judicial system. As an annually well-known international competition, COLIEE aims t…

DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment

Haitao Li, Qingyao Ai, Xinyan Han +5

Recent research demonstrates the effectiveness of using pre-trained language models for legal case retrieval. Most of the existing works focus on improving the representation abili…

cs.IR2024★ 3 cited

Sequential Recommendation with Latent Relations based on Large Language Model

Shenghao Yang, Weizhi Ma, Peijie Sun +4

Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods r…

cs.IR2024★ 2 cited

Common Sense Enhanced Knowledge-based Recommendation with Large Language Model

Shenghao Yang, Weizhi Ma, Peijie Sun +4

Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performan…

A Situation-aware Enhancer for Personalized Recommendation

Jiayu Li, Peijie Sun, Chumeng Jiang +3

When users interact with Recommender Systems (RecSys), current situations, such as time, location, and environment, significantly influence their preferences. Situations serve as t…